Non-invasive determination of likely response to anti-diabetic therapies for cardiovascular disease

ABSTRACT

Provided herein are methods and systems for making patient-specific therapy recommendations of an anti-diabetic therapy for patients with known or suspected cardiovascular disease, such as atherosclerosis.

CLAIM OF PRIORITY

This application claims the benefit of U.S. Provisional Application Ser.No. 63/209,164, filed on Jun. 10, 2021 and U.S. patent application Ser.No. 17/693,229, filed on Mar. 11, 2022. The entire contents of theforegoing are incorporated herein by reference.

TECHNICAL FIELD

This disclosure relates to methods and systems for makingpatient-specific therapy recommendations for patients with known orsuspected cardiovascular disease, such as atherosclerosis.

BACKGROUND

Myocardial infarction (MI) and ischemic stroke (IS), major consequencesof unstable atherosclerotic lesions, are the most common causes of deathworldwide worldwide (World Health Organization (WHO). Cardiovasculardiseases (CVDs) Fact Sheet, 2017, 23 Apr. 2020; available online atwho.int/en/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds)).Guidance for prevention of MI and IS is currently based on treatmentefficacy at the group level.

According to the World Health Organization (WHO), cardiovascular disease(CVD), encompassing, coronary, and lower extremity artery disease, isthe leading cause of death and disability globally (The Atlas of HeartDisease and Stroke, W.H. Organization, Editor, 2014), mainly bymyocardial infarction and ischemic stroke from unstable atherosclerosisworldwide (World Health Organization (WHO). Cardiovascular diseases(CVDs) Fact Sheet, 2017, 23 Apr. 2020; Available from:www.who.int/en/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds)).New treatments have been revolutionary over the last 30 years, yet CVDstill exerts an exorbitantly high financial costs (Bloom et al., TheGlobal Economic Burden of Noncommunicable Diseases, W.E. Forum, Editor.2011: Geneva), with a $320 billion annual burden on the U.S. economyalone (Mozaffarian et al., Heart Disease and Stroke Statistics-2015Update: A Report from the American Heart Association. Circulation, 2015.131(4): p. e29). This is exacerbated by aging and changing ethnic mix(Gierada et al., Projected outcomes using different nodule sizes todefine a positive CT lung cancer screening examination. Journal of theNational Cancer Institute, 2014. 106(11): p. dju284; Warner, J. StrokeCosts Reaching Trillions: Without Action, Financial Costs of Strokes toReach $2.2 Trillion by 2050. Stroke Health Center 2006 (cited 2014 Nov.14, 2014); Available from:www.webmd.com/stroke/news/20060816/stroke-costs-reaching-trillions), aswell as affecting an increasing proportion of people globally aseconomic development continues to narrow the gap between the developedand developing word populations.

In the U.S., the American Heart Association (AHA) projects that over 9%of adults are at significant (more than 20%) risk of adverse eventswithin 10 years and over 25% more are at a moderate risk (Association,A.H., AHA STATISTICAL UPDATE Heart Disease and Stroke Statistics—2018Update. Circulation Journal, 2018. 137). This yields 23 million highrisk patients and 57 million moderate risk people. Of these,approximately 30 million people in the U.S. are currently on statintherapy in an attempt to avoid new or recurrent CV events, and the 16.5million with a current CVD diagnosis are almost all on maintenancemedications (Ross, G., Too Few Americans Take Statins, CDC StudyReveals. American Council on Science and Health, 2015; Vishwanath, R.and L. C. Hemphill, Familial hypercholesterolemia and estimation of USpatients eligible for low-density lipoprotein apheresis after maximallytolerated lipid-lowering therapy. Journal of Clinical Lipidology, 2014.8: p. 18-28; Herper, M. How Many People Take Cholesterol Drugs? Forbes,2008; Pearson et al., Markers of Inflammation and CardiovascularDisease: Application to Clinical and Public Health Practice: A Statementfor Healthcare Professionals From the Centers for Disease Control andPrevention and the American Heart Association. Circulation, 2003.107(3): p. 499-511).

According to the WHO, stroke accounts for 10% of all deaths across theglobe, causing at least 5.5 million deaths annually (The Atlas of HeartDisease and Stroke, W.H. Organization, Editor. 2014). Of theapproximately 800,000 annual strokes in the U.S., 87% are ischemic, andapproximately 15% of all strokes are heralded by a transient ischemicattack (TIA) (Writing Group, M., D. Mozaffarian et al., Heart Diseaseand Stroke Statistics-2016 Update: A Report From the American HeartAssociation. Circulation, 2016. 133(4): p. e38-360; Bruce Ovbiagele,Stroke Epidemiology: Advancing Our Understanding of Disease Mechanismand Therapy. Neurotherapeutics, 2011. 2011(8): p. 319-329). Manyischemic stroke events are caused by atherosclerosis (Barrett et al.,Stroke Caused by Extracranial Disease. Circ Res, 2017. 120(3): p.496-501). 2.3 million subjects in the US are believed to have clinicallysignificant stenosis (>50%), 19% of which have over 70% stenosis (deWeerd et al., Prevalence of Asymptomatic Carotid Artery Stenosis in theGeneral Population: An Individual Participant Data Meta-Analysis.Stroke, 2010. 41(6): p. 1294-1297). Stroke also results in enormouscosts for society, accounting for $36.5 (Go et al., Heart Disease andStroke Statistics 2014 Update: A Report From the American HeartAssociation. Circulation, 2014. 129(3): p. e28-e292) to $74 billionannually (D. L. Brown et a., Projected costs of ischemic stroke in theUnited States. Neurology, 2006), estimated to reach $2.2 trillion by2050 (PTINR.com-Staff $2.2 trillion stroke cost projected. 2006; Brownet al., Projected costs of ischemic stroke in the United States.Neurology, 2006. 67(8): p. 1390-1395).

According to the WHO, “coronary heart disease is now the leading causeof death worldwide. It is on the rise and has become a true pandemicthat respects no borders” (The Atlas of Heart Disease and Stroke, W.H.Organization, Editor. 2014). Of the approximately 1.2 million annualcoronary attacks in the U.S., ˜66,000 are new, ˜305,000 are recurrent,and ˜160,000 are silent myocardial infarctions (MIs) (Writing Group,Mozaffarian et al., Heart Disease and Stroke Statistics-2016 Update: AReport From the American Heart Association. Circulation, 2016. 133(4):p. e38-360; Bruce Ovbiagele, Stroke Epidemiology: Advancing OurUnderstanding of Disease Mechanism and Therapy. Neurotherapeutics, 2011.2011(8): p. 319-329. Coronary heart disease caused by atherosclerosis isthe most common type of heart disease, killing 365,914 people in 2017(Benjamin et al., Heart Disease and Stroke Statistics-2019 Update: AReport From the American Heart Association. Circulation, 2019. 139(10):p. e56-e528).

The relative risk levels for varying degrees of obstruction remainsequivocal, with some reports seeming to support the notion thatclinically non-obstructive coronary artery disease (CAD) actuallyharbors more high-risk plaque than more occlusive plaques, where otherssuggest that the stenotic plaques do have higher event rates (Chang etal., Coronary Atherosclerotic Precursors of Acute Coronary Syndromes.JOURNAL OF THE AMERICAN COLLEGE OF CARDIOLOGY (JACC), 2018. 71(22);Gaston A. Rodriguez-Granillo et al., Defining the non-vulnerable andvulnerable patients with computed tomography coronary angiography:evaluation of atherosclerotic plaque burden and composition. EuropeanHeart Journal— Cardiovascular Imaging, 2016. 2016(17): p. 481-491;Ahmadi et al., Do plaques rapidly progress prior to myocardialinfarction? The interplay between plaque vulnerability and progression.Circulation research, 2015. 117(1): p. 99-104; Bittencourt et al.,Prognostic Value of Nonobstructive and Obstructive Coronary ArteryDisease Detected by Coronary Computed Tomography Angiography to IdentifyCardiovascular Events. Circulation: Cardiovascular Imaging, 2014. 7(2):p. 282-291; Virmani et al., Pathology of the Vulnerable Plaque. JACC,2006. 47(8): p. C13-8; F D Kolodgie et al., Pathologic assessment of thevulnerable human coronary plaque. Heart, 2004. 90; Virmani et al.,Lessons from sudden coronary death: a comprehensive morphologicalclassification scheme for atherosclerotic lesions. Arterioscler ThrombVasc Biol, 2000. 20(5): p. 1262-75).

There is a significant need to help healthcare providers maketherapeutic recommendations that are tailored to specific patientsrather than taking a “one size fits all” approach with the available andfuture therapies for cardiovascular disease.

SUMMARY

The present disclosure provides methods and systems for selecting andrecommending a suitable therapeutic treatment plan for a patient withcardiovascular disease, such as atherosclerosis. For example, physiciansand other healthcare providers can use the new methods and systems toanalyze and process non-invasively obtained data, such as imaging data,e.g., computed tomography angiography (CTA) data, of arteries frompatients with atherosclerosis to obtain predicted proteomic and genomicinformation. Based on this information, various potential therapies,e.g., pharmacotherapies and/or procedural interventions, can besimulated based on their mechanisms of action in in silico systemsbiology models as described herein to enable the health care provider toprovide a report to the patient recommending one or more specificpharmacotherapies and/or procedural interventions to be used to treatthe patient.

This disclosure also provides methods for obtaining proteomic and/orgenetic information and methods for building in silico systems biologymodels.

The in silico systems biology models are initially generated or trainedwith two types of data. First, one uses experimentally determined datafrom biological specimens from development subjects. Developmentsubjects are people for whom actual proteomic data is available thatshows differentially expressed protein levels that are linked to thespecific characteristics and morphology of the plaques in each of thosesubjects. Second, one uses results from searches of public literature,experimental results, and/or other databases to find journal articlesand the like to obtain detailed information about the proteins in themodel. These two sources of data are used to create the initial insilico systems biology model.

The initial in silico systems biology model is then updated withcalibration data, such as ′omics data, from test subjects to validateand refine the initial model. The calibration data is again based onactual biological samples that show differentially expressed proteinand/or transcription levels that are linked to the specificcharacteristics and morphology of the plaques in each of those testsubjects. This update of the initial model provides a calibrated insilico systems biology model. This step confirms that the model works asintended and also augments and renders the model more robust, given thecalibration data from many test subjects.

Then in operation, the calibrated in silico systems biology model isagain updated, but now with patient-specific personalized data based onimaging of the patient's plaque without the need to perform an invasiveblood test or biopsy. The calibrated in silico systems biology model isalso updated with the predicted effects of two or more differenttherapies. The methods and systems described herein use the patient'snon-invasively obtained data, e.g., imaging data to provide a therapyrecommendation based upon an automated comparison of the two or moredifferent therapies whose predicted effects are programmed into themodel.

In one aspect, the disclosure features methods of providing arecommendation of an anti-diabetic therapy for a patient with known orsuspected atherosclerotic cardiovascular disease, the method including:receiving non-invasively obtained data related to a plaque from thepatient; accessing a systems biology model of atheroscleroticcardiovascular disease, wherein (i) the systems biology model representsa plurality of pathways associated with atherosclerotic cardiovasculardisease, (ii) the plurality of pathways correspond to one or more ofMTOR, NFκβ1, ICAM1, or VCAM1, and (iii) the systems biology modelincludes a disease-associated molecule level for each molecule in thesystems biology model; updating the systems biology model usingpersonalized molecule levels derived from the non-invasively obtaineddata from the patient to generate a patient-specific systems biologymodel; updating the patient-specific systems biology model withinformation relating to an effect on glucose levels by an anti-diabeticagent based on a known mechanism of action of the anti-diabetic agent;simulating a therapeutic response by the patient to the anti-diabeticagent in the updated patient-specific systems biology model to obtain asimulated therapeutic effect; comparing the updated patient-specificsystems biology model with and without the simulated therapeutic effect;and providing a report recommending the anti-diabetic agent for thepatient when the comparison indicates an improvement for the patient.

In some aspects, the molecule is a gene, a protein, or a metabolite. Insome aspects, simulating the therapeutic response comprises settingdecreased levels of molecules related to plaque instability and settingincreased levels of molecules related to plaque stability in the atleast one network.

In some aspects, updating the systems biology model using personalizedmolecule levels comprises using disease gene transcript levels, diseaseprotein levels, or a combination of both derived from the non-invasivelyobtained data.

In some aspects, the non-invasively obtained data is imaging data, suchas radiological imaging data that can be obtained by computed tomography(CT), dual energy computed tomography (DECT), spectral computedtomography (spectral CT), computed tomography angiography (CTA), cardiaccomputed tomography angiography (CCTA), magnetic resonance imaging(MRI), multi-contrast magnetic resonance imaging (multi-contrast MRI),ultrasound (US), positron emission tomography (PET), intra-vascularultrasound (IVUS), optical coherence tomography (OCT), near-infraredradiation spectroscopy (NIRS), or single-photon emission tomography(SPECT) diagnostic images, or any combination thereof.

In some embodiments, the methods further include processing thenon-invasively obtained imaging data to obtain quantitative plaquemorphology data including structural anatomy data, tissue compositiondata, or both. For example, the structural anatomy data can include datarelating to a level of any one or more of remodeling, wall thickening,ulceration, stenosis, dilation, or plaque burden. In some embodiments,the tissue composition data includes data relating to a level of any oneor more of calcification, lipid-rich necrotic core (LRNC), intraplaquehemorrhage (IPH), matrix, fibrous cap, or perivascular adipose tissue(PVAT).

In some aspects, the pathways are compartmentalized into cell-specificnetworks. For example, the cell-specific networks can include at leastan endothelial cell network, a macrophage network, and a vascular smoothmuscle cell network.

In some aspects, the anti-diabetic agent is metformin.

In some aspects, the method further includes recommending a combinationof the anti-diabetic agent and one or both of a lipid-lowering drug andan anti-inflammatory drug.

In some embodiments, simulating the therapeutic response for theanti-diabetic agent in the patient-specific systems biology modelincludes: determining a set of molecules known to be affected by theanti-diabetic agent; defining a therapeutic effect molecule level foreach molecule in the set of molecules based on one or more knownmechanisms of action of the anti-diabetic agent on the set of molecules;and estimating a therapeutic effect molecule level for moleculesrepresented in the patient-specific systems biology model other than inthe set of molecules, based on a simulated effect of the definedtherapeutic effect molecule levels of the set of molecules on one ormore of the other molecules represented in the network.

In some aspects, the at least one network includes one or more pathwaysrepresented in Table 5 or Table 6 that are affected by glucose levels.

In another aspect, the disclosure also provides methods of identifyingone or more contraindications associated with an anti-diabetic therapyfor a patient diagnosed with atherosclerotic cardiovascular disease, themethods including: receiving non-invasively obtained data related to aplaque from the patient; accessing a systems biology model ofatherosclerotic cardiovascular disease, wherein (i) the systems biologymodel represents a plurality of pathways associated with atheroscleroticcardiovascular disease, (ii) the plurality of pathways correspond to oneor more of MTOR, NFκβ1, ICAM1, or VCAM1, and (iii) the systems biologymodel includes a disease-associated molecule level for each molecule inthe systems biology model; updating the systems biology model usingpersonalized levels of molecules derived from the non-invasivelyobtained data from the patient to generate a patient-specific systemsbiology model; updating the patient-specific systems biology model withinformation relating to an effect on glucose levels by an anti-diabeticagent based on a known mechanism of action of the anti-diabetic agent;simulating a therapeutic response by the patient to the anti-diabeticagent in the updated patient-specific systems biology model to obtain asimulated therapeutic effect; comparing the updated patient-specificsystems biology model with and without the simulated therapeutic effect;identifying any one or more contraindications associated with theanti-diabetic agent based on the comparison; and providing a reportindicating contraindications associated with the anti-diabetic agent forthe patient.

In some embodiments, the molecule is a gene, a protein, or a metabolite.

In certain embodiments of the methods, the anti-diabetic agent ismetformin.

In some embodiments, the at least one network includes one or morepathways represented in Table 5 or Table 6 that are affected by glucoselevels.

Also provided herein are methods of screening a candidate anti-diabeticagent for atherosclerotic cardiovascular disease, the method including:receiving non-invasively obtained data related to a plaque from each ofa plurality of test subjects who have been diagnosed withatherosclerotic cardiovascular disease; accessing a systems biologymodel of atherosclerotic cardiovascular disease, wherein (i) the systemsbiology model represents a plurality of pathways associated withatherosclerotic cardiovascular disease, (ii) the plurality of pathwaysinclude one or more pathways corresponding to potential targets of thecandidate anti-diabetic agent, and (iii) the at least one networkincludes a disease-associated molecule level for each molecule in thesystems biology model; updating the systems biology model usingdisease-associated molecule levels derived from the non-invasivelyobtained data from the test subjects to generate a validated systemsbiology model; updating the validated systems biology model withinformation relating to an effect on glucose levels by a candidateanti-diabetic agent based on a known mechanism of action of thecandidate anti-diabetic agent; simulating a therapeutic response to thecandidate anti-diabetic agent in the updated and validated systemsbiology model to obtain a simulated therapeutic effect; comparing atherapeutic effect in the updated and validated systems biology modelbefore and after simulating the therapeutic response by the candidateanti-diabetic agent; and providing a report indicating the candidateanti-diabetic agent is a potential therapeutic agent when the comparisonindicates the candidate anti-diabetic agent provides an glucose loweringeffect.

In some embodiments, the molecule is a gene, a protein, or a metabolite.

In certain embodiments, the anti-diabetic agent is metformin.

In some embodiments, the at least one network includes one or morepathways represented in Table 5 or Table 6 that are affected by glucoselevels.

In another aspect, the disclosure also provides methods of screening apotential subject for enrollment in a clinical trial testing safety orefficacy, or both, of a candidate anti-diabetic agent foratherosclerotic cardiovascular disease, the methods including: receivingnon-invasively obtained data related to a plaque from the potentialsubject; accessing a systems biology model of atheroscleroticcardiovascular disease; updating the systems biology model usingpersonalized molecule levels derived from the non-invasively obtaineddata from the potential subject to generate a subject-specific systemsbiology model; updating the subject-specific systems biology model withpredicted molecular levels derived from information relating to aneffect on glucose by a candidate anti-diabetic agent based on a knownmechanism of action of the candidate anti-diabetic agent; simulating atherapeutic response by the potential subject to the candidateanti-diabetic agent in the updated subject-specific systems biologymodel to obtain a simulated therapeutic effect; comparing the updatedsubject-specific systems biology model with and without the simulatedtherapeutic effect; and providing a report indicating whether thepotential subject's atherosclerotic cardiovascular disease would likelybe improved or unaffected by the candidate anti-diabetic agent, and/orwhether the potential subject would suffer an adverse effect from thecandidate anti-diabetic agent.

Definitions

A “computational model” uses computer programs to simulate and studycomplex systems using an algorithmic or mechanistic approach.

A “predictive model” is a mathematical formulation often described asartificial intelligence, machine learning, or deep learning thatcomputes one or more outputs (“response variables”) form one or moreinputs (“predictors”). In the present application, predictive models maybe used for characterizing tissue (as a “virtual tissue model”), forpredicting molecular levels form characterized tissues, or predictingoutcome form either tissue characterizations and/or virtual ′omics.

A “systems biology model” refers to a model that is used to represent aset of interconnected biological pathways potentially used to simulatechanges across those pathways under defined conditions.

An “in silico systems biology model” refers to a computationalrepresentation of a biological system, e.g., wherein the biologicalsystem is atherosclerotic cardiovascular disease.

An “initial in silico systems biology model” refers to an in silicosystems biology model generated or trained with actual proteomic dataobtained from development subjects and information obtained fromliterature searches.

A “calibrated in silico systems biology model” refers to an initial insilico systems biology model that is updated using measured calibrationdata, such as ′omics data, from a given subject (e.g., a test subject)who has been diagnosed with cardiovascular disease or from a patientwith known or suspected cardiovascular disease.

“Calibration data” refers to test subject-derived data orpatient-specific data that can be used to update an in silico systemsbiology model. Examples include measured ′omics data, such as,transcriptomics data, proteomics data, and/or metabolomics data, e.g.,obtained non-invasively. Calibration data can also be obtained frommolecular or tissue assays, e.g., biopsies.

“′Omics data” refers to biologically relevant quantities of geneexpression, transcriptomics, proteomics, or metabolomics, based ondirectly measured molecular expression levels, e.g., by blood tests,molecular assays, or tissue biopsy.

“Virtual ′omics data” refers to computationally predicted levels ofbiologically relevant quantities of gene expression, transcriptomics,proteomics, or metabolomics (e.g., based on patient-derived imagingdata) instead of directly measured molecular expression levels, e.g., byblood tests, molecular assays, or tissue biopsy.

A “network” refers to a graphical representation of interactions (edges)between various molecules (nodes).

An “artificial neural network” refers to a type of computational model,wherein the computational model is structured analogously to the humanbrain, as a series of interconnected “neurons” or mathematically assummations by weights and thus providing means to represent complexrelationships with high degrees of non-linearity.

“Direction (of the edge)” refers to an orientation of an interactionbetween a pair of molecules (e.g., when molecule A activates molecule B,the direction would be A to B).

A “biological pathway” refers to a series of actions among moleculesthat leads to a certain product or a change.

“Baseline level” (of a molecule) refers to the biological state (e.g.,expression level) of a molecule before perturbation in a systems biologymodel (e.g., in a healthy person or subject, before a test subject orpatient was afflicted with a disease, or before a patient started a newtreatment for a diagnosed disease).

A “disease-associated level” (of a molecule) refers to the quantitativeamount of a molecule (gene transcript, protein, or metabolite) from anindividual test-subject who has been diagnosed with a specific disease.In some instances, disease-associated levels of a molecule can bedetermined based on virtual ′omics data, which can include data obtainedfrom plaque tissue, and may also include data from minimal diseasetissue, as long as the data is taken from a test subject who has beendiagnosed with the disease, e.g., a cardiovascular disease. Note thatduring model generation, disease-associated levels from test subjectsare utilized, but during operation in the clinic personalized levels areused, where the word “calibration” applies to both in context.

A “personalized level” (of a molecule) refers to the quantitative amountof a molecule (transcript, protein, or metabolite) from an individualpatient. In some instances, personalized levels of a molecule can bedetermined based on virtual ′omics data. Note that during modelgeneration, disease-associated levels from test subjects are utilized,but during operation in the clinic personalized levels are used, wherethe word “calibration” applies to both in context.

A “phenotype” refers to the set of observable characteristics of anindividual resulting from the interaction of its genotype with theenvironment. In this specification, it can be understood as alsoreferring to “endotype” (a subtype of a disease condition, which isdefined by a distinct pathophysiological mechanism), or “theratype” (ameans to group according to their response to specific therapeuticalternatives), terms that are sometimes used in the field of precisionmedicine pertaining to the categorization or typing performed withoutloss of generality by the methods and systems described herein.

A “biochemical reaction” refers to an interaction among molecularquantities such as molecules (e.g., transcripts, RNAs, proteins,metabolites, inorganic compounds, etc.). Specifically, it refers to thetransformation of one molecule to a different molecule inside a cell,usually (though not necessarily) annotated with quantitativecoefficients or terms that allow effects to propagate across networks.

A “biochemical relation” is a semi-quantitative approximation to abiochemical reaction. “Reaction” and “relation” are used as alternativesin this disclosure (i.e., interchangeably) without loss of generality.

The new methods and systems described herein provide numerous advantagesand benefits as well as improvements in the ability to providepatient-specific recommendations of therapies for atheroscleroticcardiovascular disease.

The number of people with atherosclerosis is very high. Most patientsare unaware of their disease progression until onset of symptoms. Riskmanagement of patients is largely dependent on population-based scoringmethods such as Framingham Risk Score (Newby et al., Coronary CTAngiography and 5-Year Risk of Myocardial Infarction. N Engl J Med,2018. 379(10): p. 924-933; Bergstrom et al., The Swedish CArdioPulmonaryBioImage Study: objectives and design. J Intern Med, 2015. 278(6): p.645-59) and development of diagnostics for more precise patientcategorization is warranted. As treatment options for patients with CVDhave become available, stratifying patients increasingly needs to bebased on per-patient rather than population-based risk factors/scoringor simplistic imaging methods. For example, accessing a degree ofstenosis, calcium scoring, or even fractional flow reserve (FFR) are notsufficiently specific for determining individual patient diseasecategory at a level necessary to identify what treatment will best servethem, that is, to select among waiting, pharmacotherapy, proceduralintervention, surgery, or a specific treatment within one of thesecategories. This is important economically as well as clinically,because recent advances in pharmaceuticals targeting specific mechanismswith increasing efficacy are generally more expensive than earliergeneration drugs such as statins and are too expensive for use in broadpopulations. These new drugs are also not necessarily the best therapyfor all patients and the present methods and systems can be used tomatch the right patients with the best therapies.

One current difficulty is that an ability to measure a response to aspecific drug therapy remains elusive, and both under-treatment as wellas over-treatment remain common problems, which can result in highnumbers of patients that are needlessly treated while at the same timeconsuming financial resources and causing patients to go throughneedlessly invasive procedures for the results obtained. Likewise, tothe extent that methods are proposed to assess vulnerable plaque, thereremains the issue that just because a vulnerable plaque can be found,the causes for it are systemic rather than focal; often resulting infocal treatment being mismatched with the actual cause of the plaque,which can rather warrant systemic treatment. The concept of the“vulnerable patient” has been discussed, but we need markers to identifysuch individuals, and we need the ability to categorize the specificmechanism causing their vulnerability at an individual level, if we areto make demonstrable improvements in outcomes for given societal cost,for example by tailored therapeutics. Each of these needs andopportunities presents a challenge to the methods that have beendeveloped so far, but are addressed by the methods and systems describedherein.

The present disclosure fills gaps in understanding the extent and rateof progression of atherosclerosis under differing potential treatmentalternatives. Advanced software-based techniques to extract dataembedded in images, which are otherwise not readily appreciated visuallyor quantitatively, provide biomarkers to identify patients with unstableatherosclerosis and imaging to localize unstable atheroscleroticplaques, and provide more accurate characterizations extending fromclinical care to developing drugs that are more effective for patientsat risk of ischemic events.

The new methods and systems described herein provide outcome and costimprovements including improved noninvasive diagnostics to identifywhich patients have progressing disease, and the ability to provideautomated recommendations of the best therapy or combination therapy foreach specific patient based on simulations of how a specific therapy islikely to affect the specific patient and how the patient will respondgiven a specific therapy. The methods and systems can also be used toselect or modulate doses of specific medications based on simulatedpatient responses, as well as be used to simulate the effects of newdrug candidates, i.e., virtual clinical trials.

The presently described virtual biomarkers can go beyond indicating thatthere is a problem, to categorizing patients specifically as to the mosteffective way to treat the problem. Moreover, manifestations ofconditions are considered both in terms of dynamic insufficiency (e.g.,stress-induced ischemia of perfused tissues) as well as disruptiveevents such as thrombosis and rupture (i.e., causing infarction). Plasmabiomarkers serve an important role as a screening tool, but bythemselves are neither as sensitive nor as specific as knowing what ishappening within a tissue, e.g., within and surrounding a plaque (i.e.,the transcriptomics and proteomics of the tissue and the blood).

Unless otherwise defined, all technical and scientific terms used hereinhave the same meaning as commonly understood by one of ordinary skill inthe art to which this invention belongs. Methods and materials aredescribed herein for use in the present invention; other, suitablemethods and materials known in the art can also be used. The materials,methods, and examples are illustrative only and not intended to belimiting. All publications, patent applications, patents, sequences,database entries, and other references mentioned herein are incorporatedby reference in their entirety. In case of conflict, the presentspecification, including definitions, will control.

Other features and advantages of the invention will be apparent from thefollowing detailed description and figures, and from the claims.

DESCRIPTION OF DRAWINGS

This patent or application file contains at least one drawing executedin color. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the Office upon request and paymentof the necessary fee.

FIG. 1 is a high-level schematic flowchart showing how computationalmodelling can be used to express relationships among clinical,physiological, and molecular entities or concepts to describepathogenesis in diseases such as atherosclerosis spanning multiple timescales and spatial scales.

FIG. 2A is a series of non-invasive computed tomography angiography(CTA) images of an artery (left most column, labeled as column A), 3Dimage generated from the CTA (column B), 2D/axial images of the CTAimages (column C, where the white line in the images in column Bindicate the position of the section), and histological images (columnD) of tissues that have the following characteristics lipid-richnecrotic core plaque (LRNC), calcification (CALC), intra-plaquehemorrhage (IPH), matrix/fibrous tissue (MATX), and fibrouscap/perivascular adipose tissue (FC/PVAT). Specific tissues shown areexamples, without loss of generality.

FIG. 2B is a schematic image showing multiple objectively validatedmeasurements to characterize plaque morphology by analysis software.Among those measurements, tissue can be differently colored elements todefine its type, for example to be one of the following categories:LRNC, CALC, IPH, matrix, fibrous cap, and PVAT, or other relevant tissuetypes as needed. Specific tissues shown are examples, without loss ofgenerality.

FIGS. 3A-3F are a series of histology images (left column) and images ofnon-invasive computed tomography analyses (middle column) of twosubjects in a study cohort with unstable (A-C) and stable (D-F)atherosclerosis. The middle column (FIGS. 3B and 3E) show a 3D viewprovided by imaging software. The right column (FIGS. 3C and 3F) showclassifier output for stability phenotypes aligned with the 3D images,where red signifies an unstable plaque, yellow a stable plaque, andgreen minimal disease.

FIG. 4 is a workflow outlining the steps for determining a function ƒ,using training data sets to optimize models of various types, and theresults can be further applied to supervised or unsupervised clusteringfor use in virtual ′omics.

FIG. 5 is a workflow showing an example of how reactions or relations(i.e., interactions along biological pathways) are identified andconcentrations/rate constants, or other quantitative relationships areenumerated. Note that the number 116 is an example but either more orless references may be used without loss of generality, and it shouldalso be noted that whereas many resources are in the public domain,proprietary or unpublished resources can also be used without loss ofgenerality.

FIGS. 6A-6C, collectively, are images showing the workflow steps takento create an in silico systems biology model of atherosclerosis forsimulation of individual subject responses to different therapies, e.g.,pharmacotherapies and/or procedural interventions.

FIG. 6A is a schematic overview that shows how a specific systemsbiology model as described herein is created from molecular data andliterature-based sources, then how that model can be updated based ontest subject data to calibrate the initial model, and then how to updatethe calibrated model with patient imaging data and with data of the modeof action (MOA) of specific drugs that may be useful for a given patientto perturb the system to provide a per-patient simulated treatmentresponse and a resulting therapy recommendation for that patient.Specific numbers shown are examples, without loss of generality.

FIG. 6B is a schematic that shows how three types of biological data canbe derived from non-invasive radiology data using machine learningaccording to different reference truth bases. Input 1 from the figurerepresents patient data (CTAs) used not in the modeling, but to validatethe models. Result 2 from the figure is a set of structural anatomic andtissue characterizations (quantitative plaque morphology) defined byhistopathology. Results 3 and 4 represent virtual transcriptomics andvirtual proteomics data defined by and validated from inputs “B” and “C”respectively. Without loss of generality, the input in “B” can bemicroarray or RNA seq data, or other means to assay coding or non-codingRNAs, and input in “C” can be liquid chromatography mass spectrometry orother means to assay protein levels.

FIG. 6C is a schematic that shows how the results from FIG. 6B can beused to calibrate reaction or relation quantities in a systems biologymodel. Here we focus on the molecular level, where item 2 (quantitativeplaque morphology) is retained for continuity with FIG. 6B. Expressiondata 3 can be used to calibrate rate constants or the relative magnitudeor weights in relations that pertain to how one molecule effectsanother. Level data 4 can be used to calibrate level of molecules.Together these reactions/relations are interconnected to comprise thesystems biology model 5.

FIG. 7A is a block diagram of an example of a system for generating anin silico systems biology model of atherosclerotic cardiovasculardisease.

FIG. 7B is a block diagram of an example of a system for providing atherapeutic recommendation based on the in silico systems biology model.

FIG. 8A is a flowchart of an example of a process for generating an insilico systems biology model of atherosclerotic cardiovascular disease.

FIG. 8B is a flowchart of an example of a process for providing atherapeutic recommendation based on the in silico systems biology model.

FIG. 8C is a flowchart of an example of a process for providing atherapeutic recommendation based on the in silico systems biology model.

FIG. 9 is a schematic diagram of an example of system components thatcan be used to implement systems and methods.

FIG. 10 is a schematic diagram that shows examples of how pathways canbe compartmentalized into cell-specific networks, here for anendothelial cell network, a macrophage network, and a vascular smoothmuscle cell (VSMC) network. Specific cell types shown are examples,without loss of generality.

FIG. 11 is a schematic diagram that shows first level targets of anunstable subject at baseline (subject P491), represented in a layoutthat highlights compartmentalization with plasma (pink hue) with serumLDL indicated to reflect relations with proteins in the plasma membranesof Endothelial Cells (green), macrophages (orange), VSMCs (aquamarine),lymphocytes (blue) and in the extracellular region. Specificcompartments and cell types shown are examples, without loss ofgenerality.

FIG. 12 is an image showing the integrated intima network at “full”scope for an unstable subject (subject P491) in an untreated or baselinecondition. We note that other integrated networks, such as for theadventitia, media, or perivascular space can also be used without lossof generality.

FIGS. 13A and 13B are images showing individual subject calibration.FIG. 13A is a map that represents those molecules that had directmeasurements for the EC core network. FIG. 13B represents interpolatedvalues that demonstrate propagation of levels from non-interpolatedproteins according to type and weight of relation drawn from the pathwayspecification.

FIG. 14 is a heatmap identifying the top 25 proteins in terms ofvariance among the signatures in an experimental cohort as describedherein, in this case for the endothelial cell, mid scope network. Thisheatmap is shown as an example, other cell types, network scopes, orprotein levels are to be understood without loss of generality.

FIG. 15 is a heatmap identifying the top 25 proteins in terms ofvariance among the signatures in our experimental cohort, in this casefor the VSMC, mid scope network. This heatmap is shown as an example,other cell types, network scopes, or protein levels are to be understoodwithout loss of generality.

FIG. 16 is a heatmap identifying the top 25 proteins in terms ofvariance among the signatures in our experimental cohort, in this casefor the macrophage, mid scope network. This heatmap is shown as anexample, other cell types, network scopes, or protein levels are to beunderstood without loss of generality.

FIG. 17 is a heatmap identifying the top 25 proteins in terms ofvariance among the signatures in our experimental cohort, in this casefor the lymphocyte, mid scope network. This heatmap is shown as anexample, other cell types, network scopes, or protein levels are to beunderstood without loss of generality.

FIG. 18 is a heatmap identifying the top 25 proteins in terms ofvariance among the signatures in our experimental cohort, in this casefor the intima, mid-scope network. This heatmap is shown as an example,other cell types, network scopes, or protein levels are to be understoodwithout loss of generality.

FIGS. 19A and 19B are illustrations of an intima model at the “core”scope before and after simulation of treatment with intensive lipidlowering. This heatmap is shown as an example, other cell types, networkscopes, or candidate treatments are to be understood without loss ofgenerality.

FIG. 20 is a “caterpillar” chart indicting how different subjects canvary in terms of their specific plaque instability.

FIGS. 21A-21G are a series of plots showing mean absolute cohort-levelinstability from multi-level analysis across cell types and scopes.

FIGS. 22A-22F are plots showing mean relative treatment effects(positive meaning that the instability decreased).

FIG. 23 are radar charts representing degrees of absoluteatherosclerotic plaque stability for an example of a set of patients.Outer lines are better for the patient, with green signifying minimaldisease, yellow stable plaque, and red unstable plaque.

FIG. 24 are radar charts representing relative improvement aftertreatment simulation for an example of a set of patients. These are adifferent way of representing the data that is also shown on theabsolute charts, with a better visualization of the change, not just thenet effect, of the treatments respectively. Here, outer lines representmore pronounced effect, with green signifying improvement and redsignifying worsening disease.

FIGS. 25A-25C are personalized subject treatment recommendations forthree patients based on actual data.

DETAILED DESCRIPTION

The methods and systems described herein not only characterizeatherosclerosis in terms of morphology and stability based onnon-invasively obtained data, e.g., non-invasive imaging data, of apatient's arteries (using, e.g., CT angiography), but further providetherapy recommendations for individual patients, based on the nature andstability of their plaques, all using only non-invasively obtained datafrom the patient, e.g., imaging data, such as arterial imaging data. Forinstance, by obtaining genotypic and/or phenotypic information (i.e.,through virtual ′omics modeling, or based on actual measurements) for agiven patient, the new methods and systems described herein can be usedto model a patient's expected response to various therapies, includingmedicinal/pharmaceutical and interventional or procedural therapies, torecommend the therapy that is predicted to provide a superior outcomefor that specific patient.

Diagnostic accuracy is improved as the morphological and biologicalfeatures of atherosclerotic plaques can be determined by non-invasiveimaging. To do this, we have established a quantitative linkage betweenscales. Specifically, as shown in FIG. 1 , as time progresses,atherosclerosis progresses on a spatial scale starting at the molecularlevel on a time scale of seconds to minutes, and progressing to theentire person level on a time scale of months, years, and decades. Asdescribed herein, we have used computational modelling techniques toexpress relationships that span multiple time- and spatial-scales.

The need for the new methods and systems is clear. Myocardial infarction(MI) and ischemic stroke (IS), major consequences of unstableatherosclerotic lesions, are the most common causes of death worldwide.However, any recommendations available for the prevention of MI and ISare currently based only on treatment efficacy at the group level, andpractical means to tailor treatment for individual patients are apresently not available. To date, personalized treatment strategies foratherosclerotic cardiovascular disease (CVD) have not been possible.Other adverse outcomes from atherosclerosis include, without loss ofgenerality, claudication, amputation, and various presentations of aortadisease such as aneurysm.

In the setting of CVD, we have used existing biobanks containingdetailed disease-specific information at varying morphological andmolecular scales to create dedicated in silico systems biology models,with applications including evaluation of drug side effects,consideration of drug combinations, and modelling of the effect of drugsand procedural interventions on a specific patient. The ability toidentify in advance, whether an individual patient may or may notrespond to a drug has a strong value. Our inclusion of broad molecularpathway analysis provides an advantage by addressing fundamentalcomplexities needed for many clinical scenarios, when measurements ofmolecular species in plasma or tissue biopsies are not possible.

Incorporating molecular pathway analysis into an in silico settinghowever requires appreciation of the numerous structural and biologicalfeatures that characterize the unstable atheroma, where a number ofdifferent pathways interleave in a complex set of interactions. Forexample: collagen fibers confer structural stability (World HealthOrganization (WHO). Cardiovascular diseases (CVDs) Fact Sheet, see,who.int/en/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds)(2017)); collagen degradation the converse (Lambin et al., Radiomics:the bridge between medical imaging and personalized medicine. NatureReviews Clinical Oncology 14, 749-762, doi:10.1038/nrclinonc.2017.141(2017)). Reductions in atherogenic lipoproteins resulting fromphospholipid and cholesterol efflux improve stability (Lee et al.,Radiomics and imaging genomics in precision medicine. Precision andFuture Medicine 1, 10-31 (2017)); endothelial to mesenchymal transitioncan influence tissue structure with both stabilizing and destabilizingeffects (Buckler et al., Virtual Transcriptomics: Non-InvasivePhenotyping of Atherosclerosis by Decoding Plaque Biology From ComputedTomography Angiography Imaging. Arteriosclerosis, thrombosis, andvascular biology, Atvbaha121315969, doi:10.1161/atvbaha.121.315969(2021); Peyvandipour et al., Novel computational approach for drugrepurposing using systems biology. Bioinformatics 34, 2817-2825 (2018));and perivascular adipose tissue has been suggested to increase plaqueinflammation (Nguyen et al., Identifying significantly impactedpathways: a comprehensive review and assessment. Genome biology 20, 1-15(2019); Réda et al., Machine learning applications in drug development.Computational and structural biotechnology journal 18, 241-252 (2020);Pai et al., netDx: interpretable patient classification using integratedpatient similarity networks. Molecular systems biology 15, e8497(2019)), resulting in atherothrombosis, MI, or IS (Adam et al., Machinelearning approaches to drug response prediction: challenges and recentprogress. NPJ precision oncology 4, 1-10 (2020)).

According to the present disclosure, given the complexity andmultifactorial biology of atherosclerosis, comprehensive diseasemodelling as presented herein required the consideration of morecomplete biological networks than have been reported to date. To capturesufficiently granular information including prediction ofdisease-critical biological responses to different drugs, we includedbiological processes represented by pathway networks of molecularinteractions essential for disease progression.

In the present disclosure, we describe comprehensive in silico systemsbiology models of atherosclerosis using curated networks of molecularpathways to effectively describe and predict unstable disease. Usingmolecular data from plaque specimens from test subjects, we incorporateddisease-specific pathways across multiple cell types to develop anintegrated in silico systems biology model, we can then use thiscalibrated in silico systems biology model to make therapyrecommendations for individual patients. We evaluated the potential ofthe model by simulating the effects of different pharmacologicaltreatments on molecular processes relevant for stabilization ofatherosclerotic lesions, effectively predicting personalizedpharmacological effects and highlighting a potential for clinicalutility and tailored therapy for prevention or inhibition of adverseevents such as MI and IS.

The present disclosure also provides systems and methods of using thesemodels to provide patient-specific therapy recommendations forindividual patients based only on non-invasive arterial imaging data.

I. Methods of Obtaining Phenotypic/Endotypic/Theratypic Data Based onVirtual ′omics Modeling

Information about prevalent biological processes that are related toplaque characterization and stability can be obtained non-invasivelythrough virtual ′omics methods. Briefly, methods include receiving anon-invasively obtained imaging dataset for an atherosclerotic plaquefrom a subject; processing the non-invasively obtained imaging datasetto obtain quantitative plaque morphology data; processing thequantitative plaque morphology data with a virtual expression model toobtain estimated protein and/or gene expression data for the plaque fromthe subject; and generating phenotypic data for the atheroscleroticplaque from the subject based on the molecular data.

The phenotypic data refers to the set of observable characteristics ofan individual patient, test subject, or development subjects, resultingfrom the interaction of their genotype with the environment. Inparticular, the phenotypic data can include endotypic data, whichrelates to a subtype of a disease condition that is defined by adistinct pathophysiological mechanism, and/or theratypic data, which isused to group patients or test subjects according to their response tospecific therapeutic alternatives.

Non-Invasively Obtained Data

The first step in obtaining patient or subject data for the methods andsystems described herein is to obtain data non-invasively. For example,that data can be imaging data, i.e., image(s) of the plaques inarteries, and can be obtained by various methods that are well known inthe art. In some embodiments the imaging dataset is obtained byradiological methods. For instance, any of the following can beemployed: computed tomography (CT), dual energy computed tomography(DECT), spectral computed tomography (spectral CT), computed tomographyangiography (CTA), cardiac computed tomography angiography (CCTA),magnetic resonance imaging (MRI), multi-contrast magnetic resonanceimaging (multi-contrast MRI), ultrasound (US), positron emissiontomography (PET), intra-vascular ultrasound (IVUS), optical coherencetomography (OCT), near-infrared radiation spectroscopy (NIRS), orsingle-photon emission tomography (SPECT). In a particular embodiment,CTA is utilized.

For example, in one embodiment, CTA can be performed as a pre-operativeroutine procedure in the hospital using site-specific image acquisitionprotocols. CTA exams can be performed with 100 or 120 kVp, variation ofCTDIvol16cm between 13.9 and 36.9 mGy or CTDIvol32cm 7.9-28.3 mGy.Contrast injection rates and amounts followed by a saline chaser can beused as required. In general, a caudocranial scanning direction can beselected from the aortic arch to the vertex, using intravenous contrast.An axial image reconstruction of about 0.5 to about 1.0 mm, e.g., 0.65mm, 0.9 mm, or 1.0 mm can be used, and transferred into a digitalworkstation for vascular CTA image analysis.

Variations of these examples of non-invasive imaging are contemplatedand could be used by those of skill in the art.

Tissue Models

Data, such as imaging data obtained from the non-invasive imagingmethods described herein are loaded into an image processing software,e.g., ElucidVivo® (Elucid Bioimaging Inc., Boston, Mass.) software,which outlines (segments) the luminal and outer wall surfaces of thecommon, internal, and external arteries to provide quantitative plaquemorphology data. See also, U.S. Pat. Nos. 10,176,408, 10,740,880,11,094,058, and 11,087,460, each of which is incorporated herein byreference. Specifically, the software creates fully 3-dimensionalsegmentations of lumen, wall, and each tissue type at an effectiveresolution≈3× higher than the reconstructed voxel size with improvedsoft tissue plaque component differentiation relative to manualinspection. The common and internal artery are defined as a target withlumen and wall evaluated automatically and, when needed, editedmanually.

The software provides vessel structure measurements including the degreeof stenosis (calculated both by area or diameter), wall thickness(distance between the lumen boundary to outer vessel wall boundary), andremodeling index (the ratio of vessel area with plaque to a vessel areawithout plaque used as reference). Investigations in animal models andhistological analyses of human plaque lesions have characterizeddistinct, but common, structural and biological tissue characteristicssuch as enhanced inflammation, accumulation of a large lipid-rich andnecrotic central core (LRNC), intra-plaque hemorrhage (IPH), a thin andrupture-prone fibrous cap from extracellular matrix (ECM) degradation,apoptosis of smooth muscle cells (SMCs), level of calcification (CALC),matrix/fibrous tissue (MATX), and fibrous cap/perivascular adiposetissue (FC/PVAT).

The software includes algorithms to decrease blur caused by imageformation in the scanner. A patient-specific 3-dimensional point spreadfunction is adaptively determined so that image intensities are restoredto represent the original materials imaged more closely, which mitigatesartefacts such as calcium blooming, and enables discrimination of lessprominent tissue types. In particular, the image restoration isundertaken in concert with tissue characterization based onexpert-annotated histology (which includes both proteome andtranscriptome information), e.g., as described in U.S. Pat. Nos.10,176,408, 10,740,880, 11,094,058, and 11,087,460, each of which isincorporated herein by reference.

As shown in FIG. 2A, CTA can be processed to obtain 3D images. FIG. 2Aincludes four columns of images (from left to right) that show CTAimages (column A), processed images (columns B and C, and correspondinghistopathology annotation (column D). Specifically, as described above,the images in column A were processed using the ElucidVivo® software tocreate fully 3-dimensional segmentations of lumen, wall, and each tissuetype at a high resolution, as shown in columns B and C of FIG. 2A.Finally, column D of FIG. 2A shows the corresponding histologicalsections stained with Hematoxylin (LRNC, CALC), Perl's blue (IPH;arrows) and Masson's Trichrome to visualize fibrous tissue (MATX).

Processing of the CTA images allows for multiple objectively validatedmeasurements to be made, thereby permitting the characterization ofplaque morphology by CTA analysis software. These assessments includedstructural anatomy (“structure”) and tissue characterization(“composition”) as shown in FIG. 2B. FIGS. 2A and 2B both show tissuesthat have a lipid-rich necrotic core (LRNC), calcification (CALC),intra-plaque hemorrhage (IPH), matrix/fibrous tissue (MATX), and fibrouscap/perivascular adipose tissue (FC/PVAT). These specific tissues typesare provided as examples without loss of generality.

The overlapping densities of tissues such as LRNC and IPH, for example,necessitate a method for accurate classification. To avoid limitationsof conventional analysis of CTA utilizing fixed thresholds, the accuracyrequired for elucidating molecular pathways was achieved by algorithmsthat account for distributions of tissue constituents rather thanassuming constant material density ranges. In this way, the softwaremakes mathematical judgments to interpret the Hounsfield units (HU) ofadjacent voxels by maximizing criteria that mimic expert annotation atmicroscopy, simultaneously mitigating variation between scanners,reconstruction kernels, and contrast levels. In this way, the softwarefundamentally addresses subjectivity intrinsic to other analysismethods.

Processing the non-invasively obtained image data with the softwareprovides output information relating to quantitative plaque morphology,such as structural anatomy data and tissue composition data. Forexample, structural anatomy data includes measuring any one or more ofthe following in the lumen and wall: remodeling, wall thickening,ulceration, stenosis, dilation, plaque burden, or any of the measurandslisted in the Table 1 below.

As outlined in Table 1, vessel structure measurements include the degreeof stenosis (calculated both by area or diameter), wall thickness(distance between the lumen boundary to outer vessel wall boundary), andremodeling index (the ratio of vessel area with plaque to a vessel areawithout plaque used as reference).

TABLE 1 Structural Calculations of Vessel Anatomy Measurand DescriptionType and Units Lumen Area Cross-sectional area of blood mm² channelalong the vessel centerline % Stenosis (1 - ratio of minimum lumen with% (Max plaque to reference lumen without Stenosis) plaque) ×100, both byarea and by diameter Wall Area Cross-sectional area of vessel mm² minusthe Lumen Area along the vessel centerline Wall Maximum cross-sectionalwall mm Thickness thickness along the vessel centerline Max Wall Largestvalue of the wall thickness mm Thickness Plaque Burden Wall Area/(WallArea + Lumen Area) Unit-less ratio

Tissue composition data includes calcification (CALC), lipid-richnecrotic core plaque (LRNC), intra-plaque hemorrhage (IPH), andmatrix/fibrous tissue (MATX), see Table 2 below.

TABLE 2 Calculations of Tissue Characteristics Measurand BiologicalEvidence on Histopathology Calcification intimal/medial spaces withevidence of calcium primarily in the form of hydroxyapatite osteoblastsor osteoid present in above spaces no appreciable lipid or necrotictissue in above spaces Lipid-rich lipid droplets intermixed ECM (appearclear due to Necrotic removal) Core necrotic amorphous eosinophilicmaterial (LRNC) acellular often surrounded by fibrotic tissue generatedby smooth muscle cells/fibroblasts lack of microvasculature Intra-plaqueerythrocytes in the deeper regions of the plaque Hemorrhage with orwithout communication to lumen or (IPH) neovasculature Fresh: RBC isintact and unorganized Recent (5+ days): inflammatory response organizesthe RBC via hemolysis, fibroblast activity, macrophage activity MatrixNote elongated striated appearance which describe: intimal meshwork ofdense or loose, homogeneous/ organized collagen ECM (appear striated)embedded smooth muscle cells/fibroblasts (note elongated nuclei) noappreciable lipid or necrotic tissue may have microvasculature

Volume measurements, either in place of or additive to area measurementscan also be utilized. Likewise, various forms of spatially labelled datathat represent these can also be used. These specific tissues types areprovided as examples without loss of generality.

FIGS. 3A-3F show an exemplary embodiment of histology and non-invasivecomputed tomography analysis of two patients in the study cohortdescribed in the Examples below with unstable (FIGS. 3A-3C) and stable(FIGS. 3D-3F) atherosclerosis. Histology with Masson Tri Chrome stainingof the CEA specimens (FIG. 3A) showed extensive lipid-rich necrotic corewith rupture of the fibrous cap in the unstable lesion, whereas thestable example was dominated by fibrosis and abundant collagen (FIG.3D). The histological presentation of the two phenotypes corresponded toresults of non-invasive CTA analysis with the ElucidVivo software,visualized in 3D view (FIGS. 3B and 3E), and with classifier output forstability phenotype (FIGS. 3C and 3F; originally in color wherered=unstable plaque features, yellow=stable plaque features,green=minimal disease). Other stains such as H&E, Movat, or others canbe used without loss of generality.

Virtual ′Omics Models

As described in further detail below, the virtual ′omics models arebuilt from a variety of machine learning models. Briefly, any of severalmethods, devices, and/or other features are used to perform a specificinformational task (such as classification or regression) using a numberof examples of data of a given form, and are then capable of exercisingthis same task on unknown data of the same type and form from a newpatient or subject. The machine (e.g., a computer or processor) will“learn,” for example, by identifying patterns, categories, statisticalrelationships, etc., exhibited by training data. The result of thelearning is then used to predict whether new data exhibits the samepatterns, categories, and statistical relationships.

Examples of such models include neural networks, support vector machines(SVMs), decision trees, hidden Markov models, Bayesian networks, GramSchmidt models, reinforcement-based learning, genetic algorithms, andcluster-based learning. Multiple can be used to create the pool oftrained machines from which the choice is made. These can includemethods of feature selection and reduction, ranking of features, randomgeneration of feature sets, correlations among features, PCA (PrincipalComponent Analysis), ICA (Individual Component Analysis), parametervariation, and any methods known to those skilled in the art.

Supervised learning occurs when training data is labelled to reflect the“correct” result, i.e., that the data belongs to a certain class orexhibits a pattern. Supervised learning techniques include neuralnetworks, SVMs, decision trees, hidden Markov models, Bayesian networks,etc. Test data sets encompassing known class(es) can be used todetermine if a trained learning machine is able to identify patterns indata and/or classify data. The test data set is preferably generatedindependently from the training data set. Training Data sets (of knownor unknown classes) are used to train a learning machine. Regardless ofwhether the class of the data is known or unknown, the data can beadequate for training a learning machine. Unsupervised learning occurswhen training data is not labelled to reflect the “correct” result,i.e., there is no indication within the data itself as to whether thedata belongs to a class or exhibits a pattern. Unsupervised learningtechniques include Gram Schmidt, reinforcement-based learning,cluster-based learning, etc.

Thus, certain embodiments of the present invention can utilize machinelearning methods and/or deep learning methods, although these methodsare not always required in all embodiments.

In one embodiment, one or more neural network(s) can be generated and/orupdated with virtual ′omics from vascular CT images processed asdescribed in FIGS. 2A and 2B according to the Virtual Tissue Models inFIG. 6B and together comprising the Quantitative Plaque Morphology datain FIG. 6B and optionally additional covariates. One or more neuralnetworks ingest the 3D vessel image and, across multiple layers, combinethe spatially-resolved signal with the covariate information encoded asscalars (for example, vessel location, patient demographics, etc.,without loss of generality), to provide calibration data according tothe Virtual Expression and Virtual Proteomics models in FIG. 6Bproducing the individual patient calibration data comprising molecularlevel information utilized by the systems biology model.

This method overcomes two problems. First, the quantity of annotateddata, required for training, is both low and high dimensional of the CTimage volume. The present disclosure exploits the dimensionalityreduction provided by the Virtual Tissue Models, which also provide anopportunity for objective validation. We also leverage the largequantity of unlabeled vessels which is enabled from the use of thisvalidated image processing step, from which the virtual ′omics networkscan learn a rich representation of vessel structure in a semi- orself-supervised manner. Second, the output has high dimensionality. Weaddress this by employing a neural architecture that constructs a commonrepresentation of the input, which is shared across the components whichpredict individual ′omics levels.

In another embodiment, one or more deep learning network(s) can be usedfor adverse event prediction and/or drug interaction effects. The commonrepresentation described herein can be imported into a new model, whichwill use the features it provides to predict adverse events directly orafter further fine-tuning with labelled data. These features can also befused with numerical predictions from the systems biology model toestimate drug interaction effects.

In another embodiment, neural networks can be used to implement portionsor the whole of the therapy effect simulations, noting that portions ofthe systems biology model itself may be differentiable. Reactionkinetics networks are comprised fundamentally of systems of coupled ODEsand PDEs which may be implemented within neural networks to enablespeedups in both model training and model inference. Neural networks canbe employed to find favorable initializations of such reaction networksto allow optimal solutions efficiently.

Generating Phenotypic, Endotypic, and/or Theratypic Data forAtherosclerotic Plaques

The quantitative plaque morphology data (which relates, e.g., to theprofile, characterization, type of plaque) received from the processingof CTA images, as described in the section “Tissue Models” above, isprocessed against one or more virtual proteomic/transcriptomic models,as described above, to obtain estimated/predicted gene expression and/orprotein level data for the plaque from the subject. In other words, thetissue models are further processed against known gene-expression and/orknown protein levels patterns (that is, the tissue models based on theimaging data are correlated to gene-expression and/or protein levelspatterns) to generate a predicted ′omics model.

The predicted ′omics model then, in turn, allows the clinician topredict which 1) gene transcript levels are likely elevated and whichgene levels are likely decreased in the plaque and/or 2) protein levelsare likely elevated and which protein levels are likely decreased in theplaque. ′Omics levels (elevated/decreased/unchanged) are in reference toa non-atherosclerotic patient. As a result, this data providesinformation about the mechanisms related to plaque pathophysiology,plaque instability, or other relevant biological insight, therebygenerating phenotypic, endotypic, and/or theratypic data for theatherosclerotic plaque from the subject.

II. Methods of Generating an in Silico Systems Biology Models

Generating and Training an in Silico Systems Biology Model

The in silico systems biology model is initially generated or trainedwith two types of data. First, we use experimentally determined datafrom biological specimens from development subjects. Developmentsubjects are people for whom actual proteomic data is available thatshows differentially expressed protein levels that are linked to thespecific characteristics and morphology of the plaques in each of thosesubjects. Second, we use results from searches of public literature,experimental results, and/or other databases to find journal articlesand the like to obtain detailed information about the proteins in themodel. These two sources of data are used to create the initial model.

An example of a mathematical framework for multi-scale analysis is shownbelow:

$\begin{matrix}{{Predisposition}/} \\{{Expression}{x(t)}}\end{matrix}\underset{x \approx {f^{- \lambda}(y)}}{\overset{y \approx {f(x)}}{\longleftrightarrow}}\begin{matrix}{{Assay}{Tissue}/{Classify}} \\{{Phenotype}{y(t)}}\end{matrix}\underset{y \approx {g^{- \lambda}(z)}}{\overset{z \approx {g(y)}}{\longleftrightarrow}}\begin{matrix}{{Select}{Therapy}/} \\{{Outcome}{z(t)}}\end{matrix}$

The function y(t) refers to a phenotype y at time t. Function x is thecellular and molecular level at time t, and z represents thepatient-level outcome or state at time t. The present disclosureprovides systems of equations, or non-linear models, ƒ and g, where ƒdecreases in scale and g increases in scale. One example of function ƒis a predictive modeling paradigm, where y can be expressed as scalar,vector, or multidimensional data as pictured, to derive expressionprofiles, protein concentrations, or other lower level information. Oneexample of function g can also be a predictive model, but a differentmodel than ƒ, one that increases in scale. Inverse functions for ƒ and gcan also be derived.

Further detail is given in FIG. 4 . Here, steps are outlined fordetermining the function ƒ. Training data sets are used to optimizemodels of various types, and the results can be further applied tosupervised or unsupervised clustering. The resulting associations, atthe cohort or individual level, can be analyzed using techniques such asgene-set enrichment analysis (GSEA) to elucidate biological processesand molecular pathways at the cohort level, and/or in individualpatients. GSEA can be conducted, for example, using EnrichR (see,amp.pharm.mssm.edu/Enrichr), further passing results from Gene OntologyBiological process, and further by passing data to other systems such asRevigo (revigo.irb.hr) to determine, for example, non-duplicativeprocesses. Individual patient level inference can be applied where bothdegree of dysregulation as well as statistical model significance can betaken into account. This can be variously described as virtual ′omics.

The virtual ′omics models themselves can be utilized, without loss ofgenerality, for example, as follows. All or a selection of probes from amicroarray, or species from mass spectrometry, or other assay method forobtaining so-called ′omics data, can be selected. Single as well asmultiple variable regression models covering linear and non-linearmodelling techniques can be performed on predictor sets constructed froma development cohort including plaque morphology, demographics, clinical(laboratory) values, and/or other variables, in part to recognize thatclinical factors can affect the expression data or models, and toinspect what is the added value of morphology over clinical anddemographic data, and to identify when morphology and other variableshave independent information content, different predictor sets can beused, some only using plaque morphology, but others also using labvalues, demographic, and other values in composite models. Each modelresult can be output and tabulated to identify the highest-achievedperformance on a species-by-species basis.

Predictive performance can be determined based on the accuracy of theprediction relative to the true or reference values. Models can be builtwith variations, for example, differing sets of morphologicalmeasurements according to hypothesized physiological rationale,automated optimization using for example cross validation whilesimultaneously varying tuning parameter values; and/or, partitioningdata such that a training set on which the cross-validation wasperformed was strictly separated from a sequestered validation data setto test performance using locked-down models. Use of histologicallyvalidated plaque features, for example, can produce interpretablemodels, and when coupled with cross-validation, can mitigateoverfitting.

Supervised model quality (MQ) can be determined, by way of example, butnot only by this method, as the product of two measures for each modeltype. MQ for continuous estimation models was computed as the product ofconcordance correlation coefficient (CCC) and regression slope ofpredicted vs. observed for continuous value estimation (the former tomeasure the tightness of fit, but augmented by the latter to ensureproportional prediction relative to observed). MQ for dichotomizedcategorical prediction models was computed as the product of area underthe receiver characteristic curve (AUC) times Kappa for dichotomizedprediction (the former to measure the net classification performance,but augmented by the latter to ensure performance in both high and lowexpression classes).

The forgoing can be implemented using deep learning networks of variousnetwork topologies, and using either raw imagery, or enriched imagesidentified with tissue type annotations, and/or that result from spatialnormalizations such as, but not limited to, unwrapping.

Recognizing the existence of the various virtual ′omics processingsteps, the present disclosure builds on that base with further stepsthat provide further utility. For example, models of the complexbiological behavior sometimes referred to as pathways or cell signalingnetworks are described with mathematical formalisms using differentialequations or other mathematical formalisms that capture behavior such asmass transfer, reaction dynamics that stem from enzymes, variousinhibitory processes, and other approximations to biochemicalreactions/relations.

In general, the numeric variables identified are descriptive of thebehavior expected in groups of patients or animals, that is, in general,they are not applicable to a specific individual; but they do provide astructure and calibration levels for patient groups. One exampleembodiment is diagrammed in FIG. 5 , where literature references and/orin vitro studies are mined or conducted respectively to elucidate termsin the systems biology equations, for example, concentrations, levels,and/or rate constants. Specifically, as shown in FIG. 5 , the literatureis mined to identify reactions between biological molecules (left partof the figure). It should be noted that there are a number of softwaretools capable of representing this information visually andprogrammatically including such tools as Cell Designer(https://www.celldesigner.org/), cytoscape (see, cytoscape.org), etc.Specific sources and reactions shown are examples, without loss ofgenerality.

On the right side of FIG. 5 , the reactions are mapped. On the topright, shown is the relationship between TGFβ and Treg. On the middleright, shown is the relationship between TNF and foam cells, Th1, mastcells, Th17, and TACE. On the bottom right, shown is the relationshipbetween TL-6, foam cells, smooth muscle cells, and mast cells. By thismethod, as described in more detail below, these reactions were modeledand tied together in multi-compartment systems biology models, ingeneral with compartments for other organs, the plasma, etc., to theextent that they have an effect on atherosclerotic development. Partonet al., New models of atherosclerosis and multi-drug therapeuticinterventions. Bioinformatics 35, 2449-2457,doi:10.1093/bioinformatics/bty980 (2018)).

The present disclosure moves beyond patient groups to provide thefacility to reach individual patient-level results. As shown in FIG. 6A,the present disclosure provides methods to use results vectors ofvirtual ′omics and virtual ′omics data from individual patients (whetherthey be test subjects to validate the method or of course the intendedpatients seen in clinical practice this invention seeks to support) withknown or suspected CVD to train and update individual level rateconstants and concentrations respectively in the in silico systemsbiology model. This has the effect of using a systems biology modeldeveloped with generalized data (e.g., updated or calibrated using datafrom test subjects), to be further updated or calibrated for anindividual patient at a given point in time. This can be simulated outinto the future, to identify an “untreated or baseline” condition forthe patient, with or without additional simulations that perturb themodel according to a given candidate treatment's mechanism, therebysimulating the effect as if untreated or baseline, but also as iftreated by specific pharmacologic or device interventions, therebycreating a simulation for likely effect (response) of various specifictreatments. Further, the use of outcome data, compiled by machinelearned or other predictive models can be coupled with the mentionedper-treatment type simulation to generate personalized patientevent-free survival curves.

Specifically, first, as shown in FIG. 6B, there is a development cohort,shown on the left side of the image. For the development cohort,research CTA images and clinical CTA were fed into the modelingsoftware. Tissue measurements made at the first level of processingincluded structural anatomy and tissue characterization, using tissuemodeling software, which were trained using pathologist annotatedspecimens (noted as “Training CTAs” in FIG. 6B). This generatedquantitative plaque morphology data. These data were then fed forward asinputs to the models to elucidate molecular profiles determining plaquephenotype. Once a plaque is profiled and established, the experimentalworkflow utilizes a set of cases with paired transcriptomic and/orproteomic data from microarrays in a development cohort. These truthdata were used to build the virtual transcriptomic and/or proteomicmodels in the development cohort, then locked down for application tothe sequestered test patients as a validation of model capability (notedas “Validation CTAs” in FIG. 6B).

Updating the Initial in Silico Systems Biology Model

The initial model is then updated with calibration data, such as ′omicsdata, from test subjects to validate and refine the initial model. Thecalibration data is again based on actual biological samples that showdifferentially expressed protein and/or transcription levels that arelinked to the specific characteristics and morphology of the plaques ineach of those test subjects. This update of the initial model provides acalibrated model. This step confirms that the model works as intendedand also augments and renders the model more robust, given the data frommany test subjects.

Test data (e.g., from test subjects), are fed forward to obtaininformation about the plaque morphology as well as to obtain estimatedgene and/or protein measurements (see right side of FIG. 6A). Thisinformation is then fed into an in silico model, as described below, andthe in silico model is calibrated based on the information obtained inFIG. 6B. Specifically, as shown in FIG. 6C, the information about theplaque morphology as well the estimated gene and/or protein measurementsobtained in FIG. 6B are fed into the in silico model. Parton et al., Newmodels of atherosclerosis and multi-drug therapeutic interventions.Bioinformatics 35, 2449-2457, doi:10.1093/bioinformatics/bty980 (2018)).The reactions (the levels of various molecules) contained in the insilico model are then calibrated. Based on the calibration, the modelingpermits the building of biological pathways, which can predict thelevels of various molecules within the biological pathways.

More particularly, information obtained from CTA imaging is input intoan in silico systems biology model, which is a (set of) network(s)characterizing atherosclerotic cardiovascular disease, where the (each)network includes nodes (each node representing a different protein) andedges between a pair of nodes (each edge representing protein-proteininteractions in a given cell type, including “self-edges” as means torepresent the transcription/translation process). Each node in thenetwork has information representing a protein level, which can becalibrated based on data (e.g., computed tomography angiograph imagingdata of a plaque and proteomics data) from multiple test subjects.

Using the Calibrated In Silico Systems Biology Model

Then in operation, the calibrated model is again updated, but now withpatient-specific personalized data based on imaging of the patient'splaque without the need to perform an invasive blood test or biopsy. Thecalibrated model is also updated with the predicted effects of two ormore different therapies. The methods and systems described herein usethe patient's imaging data to provide a therapy recommendation basedupon an automated comparison of the two or more different therapieswhose predicted effects are programmed into the model.

For example, once the initial in silico systems biology model has beencalibrated, the biological pathways contained in the in silico systemsbiology model can then be manipulated based on various drug mechanism ofactions and the end outcome of treating a patient with a particular drugcan be simulated. Ultimately, a patient's likelihood of survival canalso be estimated based on the drug simulations and the systemautomatically provides a therapy recommendation as described in furtherdetail below.

III. Systems for Generating in Silico Systems Biology Models forAtherosclerosis

Given the above, here we disclose one example of a system to generatesuch a systems biology model. FIG. 7A is a block diagram of an exampleof a system 300 a for generating an in silico systems biology model ofatherosclerotic cardiovascular disease. The system 301 a includes aninput device 340, a network 320, and one or more computers 330 (e.g.,one or more local or cloud-based processors). The computer 330 caninclude a virtual ′omics engine 310, a network generation engine 304,and a network calibration engine 308. In some implementations, thecomputer 330 is a server. For purposes of the present disclosure, an“engine” can include one or more software modules, one or more hardwaremodules, or a combination of one or more software modules and one ormore hardware modules. In some implementations, one or more computersare dedicated to a particular engine. In some implementations, multipleengines can be installed and running on the same computer or computers.

The input device 340 is configured to obtain pathway data 302 a and testsubject data 302 b and provide the pathway data 302 a and the testsubject data 302 b to another device across a network 320. The pathwaydata 302 a include biological pathways (e.g., pathway names,identifiers) associated with atherosclerotic cardiovascular disease. Thetest subject data 302 b include data (e.g., computed tomographyangiograph imaging of a plague, proteomics, transcriptomics) frommultiple test subjects who have been diagnosed with atheroscleroticcardiovascular disease. For example, the input device 340 can include aserver 340 a that is configured to obtain the pathway data 302 a from apathway database. In some implementations, the one or more other inputdevices can access the test subject data 302 b obtained by the server340 a and transmit the obtained test subject data 302 b to the computer330 via the network 320. The network 320 represents a computer network(unlike biological networks such as a first network 306 and a secondnetwork 314) and can include one or more of a wired Ethernet network, awired optical network, a wireless WiFi network, a LAN, a WAN, aBluetooth network, a cellular network, the Internet, or other suitablenetwork, or any combination thereof.

The computer 330 is configured to obtain the pathway data 302 a and thetest subject data 302 b from the input device 340 and generate an insilico systems biology model of the disease represented by a network. Insome implementations, the computer 330 stores the pathway data 302 a andthe test subject data 302 b in a database 332 and access the database332 to retrieve desired datasets. The database 332, such as a localdatabase or a cloud-based database, can store the pathway data 302 a,the test subject data 302 b, a first network 306, a second network 314,or other suitable data.

In some implementations, the pathway data 302 a is obtained fromdifferential expression analysis. Each pathway in the pathway data 302 aincludes at least one differentially expressed molecule. For example,the computer 330 obtains first molecular expression data (e.g., geneexpression data, protein expression data) of a first set of testsubjects who have been diagnosed with the atherosclerotic cardiovasculardisease and second molecular expression data of a second set of testsubjects who do not have the atherosclerotic cardiovascular disease.Differential expression analysis identifies molecules, e.g., RNA, genesor proteins, that are differentially expressed between these two sets oftest subjects. The gene expression data is obtained from microarray, RNAsequencing, single cell RNA sequencing, or reverse transcriptase PCR.Without loss of generality, protein levels can be measured by liquidchromatography mass spectrometry (LC-MS or LS-MS/MS, for example).

The network generation engine 304 is configured to define/train asystems biology model by receiving publicly available and/orexperimentally determined data such as pathway data 302 a and generatinga first network 306. The first network 306 (also referred to as aninitial or baseline network) characterizes a baseline of the disease, asthe network is not yet calibrated using the test subject data 302 b. Insome implementations, the first network 306 is a data structure thatrepresents nodes, edges between nodes, and information (e.g., a proteinlevel) contained in each node. In some implementations, pathway data 302a are obtained from findings from scholarly literatures.

The network generation engine 304 can perform one or more tasks such asprotein isolation by cell type 304 a, pruning network 304 b,compartmentalization 304 c, and creating intima network 304 d. Theprotein isolation by cell type 304 a identifies a cell type in whicheach protein-protein interaction occurs. Referring to FIG. 10 , forexample, protein-protein interactions in endothelial cells, macrophages,and vascular smooth muscle cells (VSMC) are identified. Pruning network304 b removes non-proteins and proteins with missing information.

The compartmentalization 304 c aims to localize proteins by assigning acompartment to each protein, where the compartment includes anintracellular of each cell type (e.g., intracellular of VSMC), a cellmembrane, an extracellular space, and a compartment for blood.

Creating the intima network 304 d generates an intima network thatrepresents topologically accurate plasma interfaces, as the intimanetwork accounts for topological relationships between compartments. Theresulting intima network is referred to as the first network 306. Thefirst network 306 includes baseline levels of proteins. We note thatother integrated networks, such as for the adventitia, media, orperivascular space can also be used without loss of generality.

The virtual ′omics engine 310 is configured to receive the test subjectdata 302 b and generate virtual ′omics data 312. The test subject data302 b include computed tomography angiograph (CTA) imaging data of aplaque from the test subject, plaque morphology data, and proteomicsdata corresponding to the test subject. As shown in FIG. 6B, molecularmeasurements such as protein levels (proteomics) and gene expressions(transcriptomics) can be estimated based on a comparison between CTAimages used in training the virtual ′omics engine 310 and a patient'sCTA image not used in training. The test subject data 302 b correspondto the data used to train the virtual ′omics engine 310. Duringtraining, the virtual ′omics engine 310 identifies features in CTAimaging data (e.g., a particular plaque morphology) that are predictiveof the molecular measurements. After training, the virtual ′omics engine310 is validated, e.g., through a cross-validation scheme or usingsequestered test subjects. The virtual ′omics data 312 representestimated molecular measurements, e.g., transcript or protein levels.When measured molecular measurements are available, the measured proteinlevels can be used as an input to the network calibration engine 308.

The network calibration engine 308 is configured to receive the firstnetwork 306 and the virtual ′omics data 312 and generate a secondnetwork 314. The second network 314, updated from the first network 306using virtual ′omics data 312 derived from the test subject data 302 b,includes a disease-associated protein level for each protein in thesecond network. In some implementations, measured ′omics data, inaddition to or instead of the virtual ′omics data, are used to updatethe first network. To update the first network, the network calibrationengine 308 first identifies disease-associated protein levels for a setof proteins whose disease-associated protein levels are known from thevirtual ′omics data 312. For proteins whose disease-associated proteinlevels are unknown, the network calibration engine 308 iterativelyestimates a disease-associated protein level for a protein based on theprotein's adjacent nodes in the first network. After disease-associatedprotein levels of all proteins in the first network are found (eitherfrom the virtual ′omics data 312 or estimation), the network calibrationengine 308 outputs the second network 314. The computer 330 can storethe second network in the database 332.

The computer 330 can generate rendering data that, when rendered by adevice having a display such as a user device 350 (e.g., a computerhaving a monitor 350 a, a mobile computing device such as a smart phone350 b, or another suitable user device), can cause the device to outputdata including the first network 306 and the second network 314. Suchrendering data can be transmitted by the computer 330 to the user device350 through the network 320 and processed by the user device 350 orassociated processor to generate output data for display on the userdevice 350. In some implementations, the user device 350 can be coupledto the computer 330. In such instances, the rendered data can beprocessed by the computer 330 and can cause the computer 330, on a userinterface, to output data, e.g., visualizing the second network 314.

FIG. 8A is a flowchart of an example of a process 400 for generating acalibrated in silico systems biology model of atheroscleroticcardiovascular disease. The calibrated in silico systems biology modelis an updated model from the baseline (initial) model, which is builtbased on publicly available or otherwise known data such as pathwaydata, by using ′omics data from test subjects. The process will bedescribed as being performed by a system of one or more computersprogrammed appropriately in accordance with this specification. Forexample, the computer 330 of FIG. 3A can perform at least a portion ofthe example process. In some implementations, various steps of theprocess 400 can be run in parallel, in combination, in loops, or in anyorder.

The system obtains multiple first inputs indicative of biologicalpathways associated with the atherosclerotic cardiovascular disease(402). For example, the system queries a pathway database (e.g., theKyoto Encyclopedia of Genes and Genomes (KEGG)) to identify biologicalpathways associated with the atherosclerotic cardiovascular disease. Insome implementations, each pathway in the biological pathways includesat least one differentially expressed molecule.

To identify molecules that are differentially expressed, the systemobtains first molecular expression data of a first set of test subjectswho have been diagnosed with the atherosclerotic cardiovascular diseaseand second molecular expression data of a second set of test subjectswho do not have the atherosclerotic cardiovascular disease. The systemperforms differential expression analysis on the first and the secondmolecular expression data and identifies molecules that aredifferentially expressed. In some implementations, the first and thesecond molecular expression data are gene expression data. In someimplementations, the first and the second molecular expression data areprotein expression data.

The system generates, based on the first inputs, a first network (404).The first network includes nodes representing baseline levels ofproteins and edges representing protein-protein interactions in one ormore cell types. The first network includes proteins, genes, mRNA,nutrients, cellular events, external signals, or combinations thereoffound in the biological pathways. The system represents the proteins inthe multiple first inputs as the nodes in a graph (also referred to as astate graph), initializes a baseline level for each of the proteins,represents the protein-protein interactions as the edges in the graph,and outputs the graph as the first network. The baseline level indicatesa state of the node. The one or more cell types are associated with theatherosclerotic cardiovascular disease. In some implementations, the oneor more cell types include cell types that include at least one proteinwhose level is altered by the atherosclerotic cardiovascular disease.The one or more cell types can include, for example endothelial cells,vascular smooth muscle cells, macrophages, and lymphocytes. Other celltypes can be included without loss of generality.

In some implementations, each of the edges in the first network isdirected with a weight, where directed edges indicate a direction of theprotein-protein interaction, e.g., a molecule A activating a molecule B.The weight can indicate a type of the protein-protein interaction, e.g.,activation, inhibition, dissociation, methylation, glycosylation,translation, repression, degradation, etc. The weight is positive foractivation and translation. The weight is negative for inhibition,repression, and degradation. The edges in the first network can haveinformation indicative of a dependency condition: a molecule A interactswith a molecule B under a certain condition, e.g., the baseline level ofthe molecule B meets a threshold. The first network can be displayed ina graphical form on a user interface, e.g., using cytoscape.

The first network includes (i) a “core network” representingprotein-protein interactions unique to each respective cell type, (ii) a“mid network” representing protein-protein interactions that occur inmultiple cell types, but not all cell types, and (iii) a “full network”representing protein-protein interactions that occur in all cell types.The edges represent protein-protein interactions representing any one ofdifferent types of interactions including, for example, activation,inhibition, indirect effect, state change, binding, dissociation,phosphorylation, dephosphorylation, glycosylation, ubiquitination,and/or methylation.

The system can separately calibrate the core network, the mid network,and the full network by using the second inputs to generate a calibratedsub-network. After calibration, the system generates the second networkthat includes the calibrated sub-networks. In particular, theprotein-protein interaction of an i^(th) molecule with a j^(th) moleculeis represented as Σ_(j)w(j, i)*s_(j)(t−d(j, i)), wherein w(j, i) is aweight of the edge between the i^(th) molecule and the j^(th) molecule,s_(j) is a baseline level of the j^(th) molecule, t is a time step, andd(j, i) is a delay of the edge between the i^(th) molecule and thej^(th) molecule. The delay of the edge indicates the time step requiredfor the protein-protein interaction to be effected.

The system obtains second inputs indicative of calibration data frommultiple test subjects who have been diagnosed with the atheroscleroticcardiovascular disease (406). The second inputs include non-invasivelyobtained data, such as imaging data, for each test subject, of a plaquefrom the test subject, morphology data obtained from the plaque, andproteomics data corresponding to the plaque.

The imaging data can be obtained by computed tomography (CT), dualenergy computed tomography (DECT), spectral computed tomography(spectral CT), computed tomography angiography (CTA), cardiac computedtomography angiography (CCTA), magnetic resonance imaging (MM),multi-contrast magnetic resonance imaging (multi-contrast MRI),ultrasound (US), positron emission tomography (PET), intra-vascularultrasound (IVUS), optical coherence tomography (OCT), near-infraredradiation spectroscopy (NIRS), or single-photon emission tomography(SPECT) diagnostic images or any combination thereof.

In case that the proteomics data are not available, or in addition tothe proteomics data, the system can obtain transcriptomics data. In someimplementations, the system obtains, for at least some of the testsubjects, transcriptomics data. The transcriptomics data is obtained bymicroarray, RNA sequencing (RNA-seq), single cell RNA sequencing(scRNA-seq), reverse transcriptase PCR (RT-PCR), or any combinationthereof. In some implementations, the system obtains, for at least someof the test subjects, proteomics data, e.g., protein levels obtainedfrom protein mass spectrometry. In some implementations, the systemobtains, for at least some of the test subjects, liquidchromatography-mass spectrometry data of various molecules.

For the case where ′omics data are obtained, the first network includesnodes representing baseline levels of proteins and genes, and edgesrepresenting protein-protein interactions, gene-gene interactions, andprotein-gene interactions in the one or more cell types.

The system determines, from the second inputs, a disease-associatedprotein level for proteins in the first network (408). Thedisease-associated protein level of a specific protein corresponds toone or more of a measured protein level from tissue samples from thetest subjects, an estimated protein level based on one or more virtual′omics models of the test subjects, or a protein level corresponding tonon-invasively obtained imaging data from the test subjects. Indifferent embodiments, the specific proteins can be one or more oflipopolysaccharide-binding protein (LBP), integrin subunit alpha 2b(ITGA2B), toll like receptor 4 (TLR4), lipocalin 2 (LCN2), S100 calciumbinding protein A8 (S100A8), S100 calcium binding protein A9 (S100A9),cyclin dependent kinase inhibitor 1A (CDKN1A), matrix metallopeptidase 1(MMP1), receptor for advanced glycation end products (RAGE), hemeoxygenase 1 (HMOX1), SMAD family member 2 (SMAD2), and coagulationfactor VIII (F8). Many other molecular species are utilized by theinvention, without loss of generality; these are given by way of examplerather than being considered definitive or limiting.

The system identifies disease-associated protein levels for a set ofproteins from the second inputs, where the disease-associated proteinlevels of the set of proteins are obtained from the second inputs fromthe test subjects. The system estimates, for proteins in the firstnetwork other than the set of proteins, a disease-associated proteinlevel based on the disease-associated protein levels of a subset of theset of proteins, where the subset of the set of proteins are representedby adjacent nodes in the first network.

The system generates, based on the first network and thedisease-associated protein levels, a second network (410). The secondnetwork, an updated network from the first network using the secondinputs, represents a calibrated in silico systems biology model of theatherosclerotic cardiovascular disease and includes thedisease-associated protein level for each protein in the second network.To generate the second network, the system identifies adisease-associated protein level for each node whose disease-associatedprotein level is obtained from the calibration data from the testsubjects; and identifies a disease-associated protein level for eachnode whose disease-associated protein level is estimated.

IV. Methods and Systems for Predicting Suitable Therapeutic/TreatmentPlans for Specific Patients

In general, various therapies, e.g., pharmacotherapies and/or proceduralinterventions, can be used for the treatment of cardiovascular diseases,such as atherosclerosis. The in silico systems biology models describedherein can simulate how an actual patient will react to a particulartherapy (i.e., will the therapy have a beneficial effect, and if so, towhat extent) based on the mechanism of action of that specificpharmacotherapy. Provided below are examples of embodiments of howpersonalized therapeutic treatment plans can be simulated in the insilico systems biology model described herein by manipulating/physicallychanging the levels of certain molecules, e.g., RNA, DNA, or all orparts of genes or proteins, in the models based on the mechanism ofaction of the therapy, e.g., pharmacotherapy. Accordingly, thisdisclosure provides methods of simulating a therapeutic response in anactual patient by modulating levels of specific gene transcripts and/orprotein levels in the in silico systems biology models described herein.

FIG. 7B is a block diagram of an example of a system 300 b for providinga therapeutic recommendation for a patient with known or suspectedatherosclerotic cardiovascular disease, based on the in silico systemsbiology model. The system 300 b includes an input device 340, a network320, and one or more computers 330. The computer 330 can include avirtual ′omics engine 310, a network calibration engine 308, and atherapeutic response simulation engine 316. Engines not describedreferring to FIG. 7A are described here.

The virtual ′omics engine 310 in FIG. 7B that has been trained on thetest subject data 302 b is configured to receive patient data 302 c andgenerate virtual ′omics data 312. The patient data 302 c includes acomputed tomography angiograph (CTA) imaging dataset for anatherosclerotic plaque from the patient. Based on comparing the patientdata 302 c and the test subject data 302 b, the virtual ′omics engine310 predicts the levels of certain molecules (e.g., protein levels).

The network calibration engine is configured to receive the virtual′omics data 312 (e.g., predicted protein levels of the patient, based onthe CTA imaging dataset) and the first network 306 and generate a secondnetwork 314. The first network 306 is a trained in silico systemsbiology model of atherosclerotic cardiovascular disease, as describedreferring to FIG. 7A. The molecule levels in the first network 306 areupdated based on the multiple test subject, but not to a particularpatient yet. The network calibration engine 308 aims to update, for agiven patient, the first network 306 to generate a patient-specificnetwork, the second network 314. To generate the second network 314, thenetwork calibration engine 308 updates the molecule levels in the firstnetwork 306 based on the virtual ′omics data 312; the updated moleculelevels are referred to as personalized molecule levels. For moleculeswith missing virtual ′omics data (that is, molecules whose levels arenot predicted by the virtual ′omics engine 310), the network calibrationengine 308 estimates a personalized molecule level based on thepersonalized molecule levels of adjacent nodes in the first network. Insome implementations, the network calibration engine 308 removesmolecules whose molecule levels cannot be estimated.

The therapeutic response simulation engine 316 simulates a therapeuticresponse for each potential therapy in the second network, the trainedin silico systems biology model calibrated for a given patient. Thetherapeutic response simulation engine 316 determines a known set ofmolecules affected by the potential therapy, e.g., based on publishedscientific discoveries pertaining to mechanism of action and defines oneor more therapeutic effect molecule level for each molecule in the knownset of molecules (e.g., proteins, genes), e.g., based on knownmechanisms of action of the potential therapy. The therapeutic responsesimulation engine 316 estimates a therapeutic effect molecule levelbased on a simulated effect of the defined therapeutic effect moleculelevels and compares the defined and estimated therapeutic effectmolecule levels in the second network, before and after the therapeuticresponse simulation for each potential therapy. As an output of thetherapeutic response simulation engine 316, a therapeutic recommendation318, a report indicating the preferred therapy for the patient, isgenerated. The therapeutic recommendation 318 is sent to the user device350, e.g., the monitor 350 a and the smartphone 350 b. The therapeuticrecommendation 318 can be stored in the database 332, for the computer330 to access to retrieve.

FIG. 8B is a flowchart of example of a process 450 for providing atherapeutic recommendation for a patient with known or suspectedatherosclerotic cardiovascular disease. The process will be described asbeing performed by a system of one or more computers programmedappropriately in accordance with this specification. For example, thecomputer 330 of FIG. 7B can perform at least a portion of the exemplaryprocess. In some implementations, various steps of the process 450 canbe run in parallel, in combination, in loops, or in any order.

The system receives a non-invasively obtained imaging data of a plaquefrom the patient (452). The non-invasively obtained imaging data isobtained by computed tomography (CT), dual energy computed tomography(DECT), spectral computed tomography (spectral CT), computed tomographyangiography (CTA), cardiac computed tomography angiography (CCTA),magnetic resonance imaging (MM), multi-contrast magnetic resonanceimaging (multi-contrast MRI), ultrasound (US), positron emissiontomography (PET), intra-vascular ultrasound (IVUS), optical coherencetomography (OCT), near-infrared radiation spectroscopy (NIRS), orsingle-photon emission tomography (SPECT) diagnostic images or anycombination thereof.

The system accesses a trained in silico systems biology model ofcardiovascular disease (454). The trained in silico systems biologymodel includes a network characterizing the cardiovascular disease. Thenetwork includes a disease-associated molecule level for each of aplurality of nodes, wherein each node represents a different molecule,e.g., protein or gene or nucleic acid. In some implementations, thenetwork includes proteins, and disease molecule levels representdisease-associated protein levels for proteins and disease-associatedgene levels for genes. The network includes protein-protein interactionsin one or more cell types including endothelial cells, vascular smoothmuscle cells, macrophages, and lymphocytes. These cell types, in someimplementations, are cell types that include at least one molecule whoselevel is altered by the cardiovascular disease. In some implementations,the trained in silico systems biology model is a baseline model builtusing the publicly available or otherwise known data. In someimplementations, the trained in silico systems biology model is anupdated model, from the baseline model, using calibration data from testsubjects, as described herein.

The system updates the in silico systems biology model for the patientusing personalized molecule levels derived from the non-invasivelyobtained data, e.g., imaging data (456). The system compares the imagingdata of the patient with imaging data of multiple test subjects, wherethe imaging data of multiple test subjects were an input to update thein silico systems biology model. Based on the comparison, the systempredicts personalized molecule levels for molecules in the network.

The system obtains information relating to two or more potentialtherapies for the patient, or compares one potential therapy againstbaseline levels (458). The potential therapies can include, for example,(i) a lipid lowering drug, (ii) an antidiabetic drug, (iii) ananti-inflammatory treatment, and (iv) any combination of (i)-(iii). Forexample, the system receives an identifier of the potential therapies.

For example, the lipid-lowering drug can be any one or more of a statin,a proprotein convertase subtilisin kexin type 9 (PCSK9) inhibitor, or acholesteryl ester transfer protein (CETP). The antidiabetic drug caninclude, for example, metformin. The anti-inflammatory treatment caninclude, for example, anti-IL1β, anti-TNF, anti-IL 12/23, and anti-IL17agents. These treatments are provided as examples without loss ofgenerality.

The system simulates a therapeutic response for each potential therapyin the trained in silico systems biology model (460) by following subprocesses. The system determines a known set of molecules affected bythe potential therapy (460 a). The system defines a therapeutic effectmolecule level for each molecule in the known set of molecules based onone or more known mechanisms of action of the potential therapy on theknown set of molecules (460 b). To define the therapeutic effect level,the system sets therapeutic effect molecule levels of the set ofproteins to a baseline level. The baseline level, in someimplementations, is determined based on observed level of molecules fromsubjects or patients without disease, or a baseline can be developed forsubjects or patients already on some form of pharmacotherapy where thesimulation would be considered additive to that baseline therapy.

The system estimates a therapeutic effect level for other moleculesrepresented in the in silico systems biology model other than the set ofknown molecules, based on a simulated effect of the defined therapeuticeffect levels of the set of known molecules, e.g., proteins, on one ormore of the other molecules represented in the network (460 c). Thesystem defines a simulated therapeutic effect level for each moleculerepresented in the in silico systems biology model based on the definedand estimated therapeutic effect levels (460 d). In the cases where themolecule is a protein, a therapeutic effect level is a therapeuticeffect protein level. When the molecule is a gene, a therapeutic effectmolecule level is a therapeutic effect gene level.

The system compares the simulated therapeutic effect levels in the insilico systems biology model before and after the therapeutic responsesimulation for each potential therapy (462).

The system selects one or more of the potential therapies as a preferredtherapy based on the comparison (464).

The system provides a report recommending the preferred therapy for thepatient (466). The report includes predicted effectiveness of potentialtherapies and change in therapeutic effect molecule levels before andafter the therapeutic response simulation for the preferred therapy. Thereport, as shown in FIGS. 25A-25C, can be visualized on a userinterface. In some embodiments, the system compares a therapeutic effectlevel before and after the therapeutic response simulation for only onespecific therapy, to determine whether that therapy has a beneficialeffect for a specific patient, and if so, to what extent. This processis completed for each of the potential therapies, and then the extent oftheir respective beneficial effects, if any, are compared to select thebest therapy for the specific patient.

FIG. 8C presents another implementation for providing a therapyrecommendation. In particular, a flowchart of an example of a process470 for clinical decision support is presented. The process is describedas being performed by a system of one or more computing devicesprogrammed appropriately in accordance with this disclosure. Forexample, the computer 330 of FIG. 7B can perform at least a portion ofthe process. In some implementations, various steps of the process 470can be run in parallel, in combination, in loops, or in any order.

Operations of the system includes receiving non-invasively obtained datarelated to a plaque from a patient (472). For example, imaging data canbe received by the system. Operations also include updating a trained insilico systems biology model using personalized calibration data derivedfrom the received data to generate an in silico patient-specific systemsbiology model (474). The trained in silico systems biology modelcomprises a set of networks, wherein each network comprises a pluralityof nodes, each node representing a baseline level of a molecule, and aplurality of edges between pairs of nodes, each edge representing amolecule-molecule interaction. At least two of the nodes representmolecules whose levels are affected by the atheroscleroticcardiovascular disease. At least one of the set of networks includes adisease-associated molecule level for each of the nodes in the network.In one implementation, the at least set of networks includes of nodescorresponding, respectively, to one or more of, for example, aglycosylated low-density lipoprotein (glyLDL), an oxidized LDL (oxLDL),a minimally-modified LDL (mmLDL), or a very-low-density lipoprotein(VLDL). Operations of the system with such nodes also include perturbingthe in silico patient-specific systems biology model to simulate atherapeutic effect of, for example, a lipid-lowering agent for thepatient (476). Operations of the system that has such a perturbationalso include providing an output indicating a level of improvement inthe atherosclerotic cardiovascular disease by the exemplarylipid-lowering agent for the patient and a recommendation supporting aclinical decision as to whether the exemplary lipid-lowering agent wouldbenefit the patient (478).

FIG. 9 illustrates an example of a block diagram of system componentsthat can be used to implement systems and methods described herein. FIG.9 shows a computing device 500 that represents any one or more ofvarious forms of digital computers, such as laptops, desktops,workstations, personal digital assistants, servers, blade servers,mainframes, and other appropriate computers. Computing device 550 isintended to represent various forms of mobile devices, such as personaldigital assistants, cellular telephones, smartphones, and other similarcomputing devices. Additionally, computing device 500 or 550 can includeUniversal Serial Bus (USB) flash drives. The USB flash drives can storeoperating systems and other applications. The USB flash drives caninclude input/output components, such as a wireless transmitter or USBconnector that can be inserted into a USB port of another computingdevice. The components shown here, their connections and relationships,and their functions, are meant to be exemplary only, and are not meantto limit implementations of the inventions described and/or claimed inthis document.

Computing device 500 includes a processor 502, memory 504, a storagedevice 506, a high-speed controller 508 connecting to memory 504 andhigh-speed expansion ports 510, and a low speed controller 512connecting to low speed bus 514 and storage device 506. Each of thecomponents 502, 504, 508, 508, 510, and 512, are interconnected usingvarious busses, and can be mounted on a common motherboard or in othermanners as appropriate. The processor 502 can process instructions forexecution within the computing device 500, including instructions storedin the memory 504 or on the storage device 506 to display graphicalinformation for a GUI on an external input/output device, such asdisplay 516 coupled to high speed controller 508. In otherimplementations, multiple processors and/or multiple buses can be used,as appropriate, along with multiple memories and types of memory. Also,multiple computing devices 500 can be connected, with each deviceproviding portions of the necessary operations, e.g., as a server bank,a group of blade servers, or a multi-processor system.

The memory 504 stores information within the computing device 500. Inone implementation, the memory 504 is a volatile memory unit or units.In another implementation, the memory 504 is a non-volatile memory unitor units. The memory 504 can also be another form of computer-readablemedium, such as a magnetic or optical disk.

The storage device 506 is capable of providing mass storage for thecomputing device 500. In one implementation, the storage device 506 canbe or contain a computer-readable medium, such as a floppy disk device,a hard disk device, an optical disk device, or a tape device, a flashmemory or other similar solid state memory device, or an array ofdevices, including devices in a storage area network or otherconfigurations. A computer program product can be tangibly embodied inan information carrier. The computer program product can also containinstructions that, when executed, perform one or more methods, such asthose described above. The information carrier is a computer-ormachine-readable medium, such as the memory 504, the storage device 506,or memory on processor 502.

The high-speed controller 508 manages bandwidth-intensive operations forthe computing device 500, while the low speed controller 512 manageslower bandwidth intensive operations. Such allocation of functions isexemplary only. In one implementation, the high-speed controller 508 iscoupled to memory 504, display 516, e.g., through a graphics processoror accelerator, and to high-speed expansion ports 510, which can acceptvarious expansion cards (not shown). In the implementation, low speedcontroller 512 is coupled to storage device 506 and low speed bus 514.The low-speed expansion port, which can include various communicationports, e.g., USB, Bluetooth, Ethernet, wireless Ethernet can be coupledto one or more input/output devices, such as a keyboard, a pointingdevice, microphone/speaker pair, a scanner, or a networking device suchas a switch or router, e.g., through a network adapter.

The computing device 500 can be implemented in a number of differentforms, as shown in the figure. For example, it can be implemented as astandard server 520, or multiple times in a group of such servers. Itcan also be implemented as part of a rack server system 524. Inaddition, it can be implemented in a personal computer such as a laptopcomputer 522. Alternatively, components from computing device 500 can becombined with other components in a mobile device (not shown), such asdevice 550. Each of such devices can contain one or more of computingdevice 500, 550, and an entire system can be made up of multiplecomputing devices 500, 550 communicating with each other.

The computing device 500 can be implemented in a number of differentforms, as shown in the figure. For example, it can be implemented as astandard server 520, or multiple times in a group of such servers. Itcan also be implemented as part of a rack server system 524. Inaddition, it can be implemented in a personal computer such as a laptopcomputer 522. Alternatively, components from computing device 500 can becombined with other components in a mobile device (not shown), such asdevice 550. Each of such devices can contain one or more of computingdevice 500, 550, and an entire system can be made up of multiplecomputing devices 500, 550 communicating with each other.

Computing device 550 includes a processor 552, memory 564, and aninput/output device such as a display 554, a communication interface566, and a transceiver 568, among other components. The device 550 canalso be provided with a storage device, such as a micro-drive or otherdevice, to provide additional storage. Each of the components 550, 552,564, 554, 566, and 568, are interconnected using various buses, andseveral of the components can be mounted on a common motherboard or inother manners as appropriate.

The processor 552 can execute instructions within the computing device550, including instructions stored in the memory 564. The processor canbe implemented as a chipset of chips that include separate and multipleanalog and digital processors. Additionally, the processor can beimplemented using any of a number of architectures. For example, theprocessor can be a CISC (Complex Instruction Set Computers) processor, aRISC (Reduced Instruction Set Computer) processor, or a MISC (MinimalInstruction Set Computer) processor. The processor can provide, forexample, for coordination of the other components of the device 550,such as control of user interfaces, applications run by device 550, andwireless communication by device 550.

Processor 552 can communicate with a user through control interface 558and display interface 556 coupled to a display 554. The display 554 canbe, for example, a TFT (Thin-Film-Transistor Liquid Crystal Display)display or an OLED (Organic Light Emitting Diode) display, or otherappropriate display technology. The display interface 556 can compriseappropriate circuitry for driving the display 554 to present graphicaland other information to a user. The control interface 558 can receivecommands from a user and convert them for submission to the processor552. In addition, an external interface 562 can be provide incommunication with processor 552, so as to enable near areacommunication of device 550 with other devices. External interface 562can provide, for example, for wired communication in someimplementations, or for wireless communication in other implementations,and multiple interfaces can also be used.

The memory 564 stores information within the computing device 550. Thememory 564 can be implemented as one or more of a computer-readablemedium or media, a volatile memory unit or units, or a non-volatilememory unit or units. Expansion memory 574 can also be provided andconnected to device 550 through expansion interface 572, which caninclude, for example, a SIMM (Single In Line Memory Module) cardinterface. The expansion memory 574 can provide extra storage space fordevice 550, or can also store applications or other information fordevice 550. Specifically, expansion memory 574 can include instructionsto carry out or supplement the processes described above, and caninclude secure information also. Thus, for example, expansion memory 574can be provide as a security module for device 550, and can beprogrammed with instructions that permit secure use of device 550. Inaddition, secure applications can be provided via the SIMM cards, alongwith additional information, such as placing identifying information onthe SIMM card in a non-hackable manner.

The memory can include, for example, flash memory and/or NVRAM memory,as discussed below. In one implementation, a computer program product istangibly embodied in an information carrier. The computer programproduct contains instructions that, when executed, perform one or moremethods, such as those described above. The information carrier is acomputer- or machine-readable medium, such as the memory 564, expansionmemory 574, or memory on processor 552 that can be received, forexample, over transceiver 568 or external interface 562.

Device 550 can communicate wirelessly through communication interface566, which can include digital signal processing circuitry wherenecessary. Communication interface 566 can provide for communicationsunder various modes or protocols, such as GSM voice calls, SMS, EMS, orMMS messaging, CDMA, TDMA, PDC, WCDMA, CDMA2000, or GPRS, among others.Such communication can occur, for example, through (radio-frequency)transceiver 568. In addition, short-range communication can occur, suchas using a Bluetooth, Wi-Fi, or other such transceiver (not shown). Inaddition, GPS (Global Positioning System) receiver module 570 canprovide additional navigation- and location-related wireless data todevice 550, which can be used as appropriate by applications running ondevice 550.

Device 550 can also communicate audibly using audio codec 560, which canreceive spoken information from a user and convert it to usable digitalinformation. Audio codec 560 can likewise generate audible sound for auser, such as through a speaker, e.g., in a handset of device 550. Suchsound can include sound from voice telephone calls, can include recordedsound, e.g., voice messages, music files, etc. and can also includesound generated by applications operating on device 550.

The computing device 550 can be implemented in a number of differentforms, as shown in the figure. For example, it can be implemented as acellular telephone 780. It can also be implemented as part of asmartphone 782, personal digital assistant, or other similar mobiledevice.

Various implementations of the systems and methods described here can berealized in digital electronic circuitry, integrated circuitry,specially designed ASICs (application specific integrated circuits),computer hardware, firmware, software, and/or combinations of suchimplementations. These various implementations can includeimplementation in one or more computer programs that are executableand/or interpretable on a programmable system including at least oneprogrammable processor, which can be special or general purpose, coupledto receive data and instructions from, and to transmit data andinstructions to, a storage system, at least one input device, and atleast one output device.

These computer programs (also known as programs, software, softwareapplications or code) include machine instructions for a programmableprocessor, and can be implemented in a high-level procedural and/orobject-oriented programming language, and/or in assembly/machinelanguage. As used herein, the terms “machine-readable medium”“computer-readable medium” refers to any computer program product,apparatus and/or device, e.g., magnetic discs, optical disks, memory,Programmable Logic Devices (PLDs), used to provide machine instructionsand/or data to a programmable processor, including a machine-readablemedium that receives machine instructions as a machine-readable signal.The term “machine-readable signal” refers to any signal used to providemachine instructions and/or data to a programmable processor.

To provide for interaction with a user, the systems and techniquesdescribed here can be implemented on a computer having a display device,e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitorfor displaying information to the user and a keyboard and a pointingdevice, e.g., a mouse or a trackball by which the user can provide inputto the computer. Other kinds of devices can be used to provide forinteraction with a user as well; for example, feedback provided to theuser can be any form of sensory feedback, e.g., visual feedback,auditory feedback, or tactile feedback; and input from the user can bereceived in any form, including acoustic, speech, or tactile input.

The systems and techniques described here can be implemented in acomputing system that includes a back end component, e.g., as a dataserver, or that includes a middleware component, e.g., an applicationserver, or that includes a front end component, e.g., a client computerhaving a graphical user interface or a Web browser through which a usercan interact with an implementation of the systems and techniquesdescribed here, or any combination of such back end, middleware, orfront end components. The components of the system can be interconnectedby any form or medium of digital data communication, e.g., acommunication network. Examples of communication networks include alocal area network (“LAN”), a wide area network (“WAN”), and theInternet.

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other.

V. Types of Therapies

The in silico systems biology models described herein can be used tomodel the effects of any therapies, e.g., medical or proceduraltherapies, for which a mechanism of action is known or discovered, e.g.,for which a mechanism of action is described in the public record orotherwise known, and is converted into data that can be used to updatethe calibrated model. Then, the systems biology model can be updatedwith data representing a specific patient's plaque characteristics, andthen specific models of potential therapies can be added to the systemsbiology model updated with the specific patient's information. Theresults of applying the drug to the patient-specific systems biologymodel can be compared, and the best performing therapy, or no therapy,can be recommended to the patient.

At the outset, it is important to note that the drugtherapies/procedural interventions listed below are merely examples. Oneskilled in the art, prior to performing the methods described hereinwould do a review of the literature for drug and/or proceduralintervention therapies and would determine the necessary parameters tomodel the effectiveness of that specific drug and/or proceduralintervention therapy. For example, one of skill in the art woulddetermine which molecules represented in the trained in silico systemsbiology model to manipulate and by how much to alter their levels basedon the literature search.

A current search of the literature would show that atherosclerosis hasmany different endotypes. For example, an endotype of increased LDL isassociated with the following genetic factors: LDLR, PCSK9, APOE,APOB-100, SORT1, ANGPTL3, CELSR2, PSRC1, HMGCR; and the followingbiomarkers: Total cholesterol, LDL-C, ApoB, ApoB-100, ox-LDLs, modifiedLDL, sdLDL, and PCSK9. An endotype characterized by an increase in Lp(a)is mainly genetically determined by the LPA gene locus and is notsignificantly influenced by other genetic, dietary, or environmentalfactors.

Biomarkers associated with an increase in Lp(a) include the following:Lp(a), apolipoprotein isoform (a), and antibodies to Lp(a). An endotypeassociated with arterial injury (arterial hypertension) is associatedwith the following genetic factors: ADAMTS7, THBS2, CFDP1, NOX4, EDNRA,PHACTR1, GUCY1A3, CNNM2, CYP17A1; and the following biomarkers:endothelin, angiotensin, adrenomedul-lin, natriuretic peptides, vonWillebrand factor, cell adhesion molecules, endothelial progenitorcells, endothelial micro-particles, nitric oxide, and asymmetricdimethylarginine.

An endotype characterized by inflammation is associated with thefollowing genetics: CXCL12, MCP-1, TLRs, SH2B3, HLA, IL-6R, IL-5,PECAM1; and the following biomarkers: TNF, IL-1b, IL-6, IL-12, IL-18,IL-23, IFN-g, IL-17, IL-22, TH17 cells, hsCRP, pentraxin-3, sCD40L,VCAM, and ICAM.

Finally, the endotype characterized by metabolic risk factors isassociated with the following genetics: TCF7L2, HNF1A, CTRB1/2, MRAS,ZC3HC1, MIR17HG, and CCDC92; and the following biomarkers: bloodglucose, blood insulin, C-peptide, glycated hemoglobin, glycatedalbumin, sRAGE, fructosamine (Vadim V. Genkel, Igor I. Shaposhnik,“Conceptualization of Heterogeneity of Chronic Diseases andAtherosclerosis as a Pathway to Precision Medicine: Endophenotype,Endotype, and Residual Cardiovascular Risk”, International Journal ofChronic Diseases, vol. 2020, Article ID 5950813, 9 pages, 2020).

Examples of Drug Therapies

In general, any suitable drug therapy is contemplated by the presentapplication. For instance, any compound that targets (e.g., inhibits) aspecific gene, protein, or metabolite. “Inhibits” refers to thecompound's ability to control, prevent, restrain, arrest, regulate amolecule's function. Exemplary compounds include, small molecules,inucleic acids (e.g, interference RNA (RNAi), short interfering RNA(siRNA); a micro, interfering RNA (miRNA); a small, temporal RNA(stRNA); or a short, hairpin RNA (shRNA); small RNA-induced geneactivation (RNAa); small activating RNAs (saRNAs); messenger RNA (mRNA),inhibitory antibodies.

Hyperlipidemia Control Medications

High levels of low-density lipoprotein cholesterol (LDL) is acharacteristic feature of cardiovascular diseases, such asatherosclerosis. As such, these diseases can be treated withhyperlipidemia control medications (e.g., intensive lipid loweringtherapies, fibrates, niacin, fish oil, statins (like, atorvastatin),ezetimibe, bile acid sequestrants, a proprotein convertase subtilisinkexin type 9 (PCSK9) inhibitor, a cholesteryl ester transfer protein(CETP), adenosine triphosphate-citrate lyase (ACL) inhibitors, omega-3fatty acid ethyl esters, and marine-derived omega-3 polyunsaturatedfatty acids (PUFA).

For example, the effect that an intensive lipid lowering drug would haveon a subject can be represented in the in silico systems biology model,thereby allowing a clinician to predict whether an intensive lipidlowering drug would be beneficial to the patient. For instance, in someembodiments the levels, e.g., of the gene level, protein level, or bothlevels, of LDL are physically lowered in the in silico systems biologymodel by 75%, 50%, 40%, 30%, 25%, 20%, 10%, or 5%, depending on what isknown about how the drug affects the LDL levels. For example, if aspecific drug is considered in the literature to be effective in certainpatients when the LDL level in the patient has been reduced by 25%, thenthe model is updated to show an effective reduction of 25%. In someembodiments, the gene level, protein level, or both, of LDL products,such as, glycosylated (glyLDL), oxidized (oxLDL), and minimally-modified(mmLDL), and VLDL are also manipulated (i.e., lowered) in the in silicosystems biology model by, for example, 75%, 50%, 40%, 30%, 25%, 20%,10%, or 5%.

Lowering the levels of these molecules in the in silico systems biologymodel shows the changes in the levels of one or more genes, proteins, orboth, as well as of other molecules that are both directly andindirectly connected to the LDL mechanistic pathway. If the in silicosystems biology model shows a reduced possibility of a stroke ormyocardial infarction, then an intensive lipid lowering drug would bedeemed as beneficial to a patient. If the in silico systems biologymodel shows no change, or a worsening of one of more conditions of thepatient over time, then the intensive lipid lowering drug would not bedeemed as beneficial to the patient and would not be recommended.

Anti-Inflammatory Drugs

Inflammation is highly associated with atherosclerosis. As such,therapies that inhibit IL-1, IL1β, TNF, IL12/23, IL17, or other agentsthat affect an inflammatory cascade can be beneficial in treatingsubjects with atherosclerosis. Examples of therapies include colchicine,canakinumab, an inhibitor of a pro-inflammatory cytokine induced ondanger signaling, a pro-resolvin (e.g., omega-3 fatty acids, like,eicosapentaenoic acid (EPA), docosahexaenoic acid (DHA), ordocosapentaenoic acid (DPA)). To date, however, it has been hard toidentify which patients would benefit vs. which would not, the lattersuffering potentially dangerous side effects until or unless likelyresponse can be established. As a result these drugs are not yet widelyused, despite their apparent promise.

Accordingly, the present disclosure provides, in some embodiments,methods for simulating the effect that an anti-inflammatory drug wouldhave on a subject or patient. For example, in some embodiments, the genelevel, protein level, or both, of inflammatory molecules (such as, butnot limited to, IL-1, IL1β, TNF, IL12/23, or IL17) are also physicallymanipulated (i.e., lowered) in the in silico systems biology model by,for example, 75%, 50%, 40%, 30%, 25%, 20%, 10%, or 5%, depending on whatis known in the literature about how a specific drug affectsinflammation. For example, if a specific drug is considered in theliterature to be effective in certain patients when the IL-1, IL1β, TNF,IL12/23, or IL17 level in the patient has been reduced by 25%, then themodel is updated to show an effective reduction of 25%. Lowering thelevels of these molecules in the in silico systems biology modelsimulates the changes in gene, protein, or both, of other molecules thatare both directly and indirectly connected in the inflammatory moleculepathway. In some cases, molecular levels can be raised, for example inpro-resolvin therapies or therapies which raise HDL by way of example,without loss of generality.

Lower plaque instability is a desirable treatment outcome. That is, ifthe in silico systems biology model after an anti-inflammatory drugeffect simulation shows improvement in stability, then ananti-inflammatory drug would be deemed as beneficial to a subject.Plaque stability is quantified based on molecule levels; if the moleculelevels of a subject are similar to those from test subjects with stableatherosclerosis, the patient will likely have a relatively higher plaquestability. The relative change in plaque stability of the subject beforeand after the anti-inflammatory drug is quantified by change in moleculelevels in the in silico systems biology model.

Anti-Diabetic Drugs

Metabolic diseases and diabetes are associated with a strongly elevatedrisk of developing cardiovascular diseases, such as, atherosclerosis. Insome subjects, a critical aspect for the development and progression ofcardiovascular disease is the impaired lowering of blood glucose levels.Accordingly, in some instances, treatment with an anti-diabetic drugwould be beneficial to a subject or patient suffering from acardiovascular disease.

Accordingly, the present disclosure provide, in some embodiments,methods for simulating the effect that an anti-diabetic drug would haveon a subject. For example, in some embodiments, the gene level, proteinlevel, or both, of glucose/metabolic-related molecules (such as, but notlimited to, MTOR, NFκβ1, ICAM1, or VCAM1) are also physicallymanipulated (i.e., lowered) in the in silico systems biology model by,for example, 75%, 50%, 40%, 30%, 25%, 20%, 10%, or 5%, depending on whatis known in the literature about how a specific drug affects glucoselevels and/or metabolism. For example, if a specific drug is consideredin the literature to be effective in certain patients when the MTOR,NFκβ1, ICAM1, or VCAM1 level in the patient has been reduced by 25%,then the model is updated to show an effective reduction of 25%.Lowering the levels of these molecules in the in silico systems biologymodel shows the changes in gene, protein, or both, of other moleculesthat are both directly and indirectly connected to theglucose/metabolic-related molecule. If the in silico systems biologymodel shows that the patient would have a reduced level of diabetes,then an anti-diabetic drug would be deemed as beneficial to a subject.If the in silico systems biology model shows no change or worsening indiabetes symptoms, then an anti-diabetic drug would not be deemed asbeneficial to the patient and would not be recommended.

Other Drug Classes

Other drug classes are also contemplated. For example immunomodulatingagents, such as those that trigger innate immunity, that are immunetolerance stimulating agents, or that increase Treg activity.

Hypertensive agent (such as, ACE inhibitors) and anti-coagulating agent(agents that reduce thrombin production and/or limits the activity ofthrombin) are also envisioned.

Triggers of innate immunity and regulation of intracellular signaltransduction suggests novel targets for therapeutic treatment, includingthe inhibition of the pro-inflammatory cytokines induced on dangersignaling. As an example, stimulating immune tolerance with increasedTreg activity is being explored. As another example, clearingchylomicron remnants (large triglyceride-rich lipoproteins) isatheroprotective since chylomicron particles and the triglyceride-richparticles are directly and indirectly implicated in plaque development.

Combination Therapies

In some instances, a subject can benefit from the combination of one ormore of the above-referenced therapies. Accordingly, in someembodiments, provided are methods for simulating the effect that anintensive lipid lowering and an anti-inflammatory drug would have on asubject; intensive lipid lowering and an anti-diabetic drug would haveon a subject; an anti-inflammatory drug and an anti-diabetic drug wouldhave on a subject; or an intensive lipid lowering, an anti-inflammatorydrug, and an anti-diabetic drug would have on a subject.

For combination therapies, in determining a known set of moleculesaffected, the therapeutic response simulation engine 316 considers afirst set of molecules affected by a first therapy, a second set ofmolecules affected by a second therapy, and a third set of moleculesaffected by an interaction between the first therapy and the secondtherapy. After defining the known set of molecules, the therapeuticresponse simulation engine 316 defines a therapeutic effect moleculelevel for each molecule in the known set of molecules, based on knownmechanisms of action of a given combination therapy. Additional stepsafter defining the therapeutic effect molecule level are describedabove, referring to FIG. 8B.

Procedural Interventions

In some embodiments, a pharmacotherapy is not the suitable treatmentplan for a given patient and a procedural intervention is the onlychoice. If the simulations in the in silico systems biology model forthe various possible drug candidates for a given patient do not show anypredicted benefit for the patient, then a procedural intervention shouldbe considered. In general, procedural interventions can makelarger-scale changes than pharmacotherapy, for example, outright tissueremoval represented by a broad decrease in protein levels, or structuralanatomic changes such as the inclusion of a stent, which can block orinterfere with connections in the systems biology model. In either case,there can also be localized drug addition, such as drug-eluting stents(DES), which may not address a current condition, but a known consequentaction by the biology, in reaction to the procedural intervention, whichcan be compensatory, but have its own undesired side effects.Perturbations or changes can be made in the trained systems biologymodel to represent various aspects of such procedural interventions.

Procedural interventions, include, but are not limited to surgery, DES,atherectomy devices, intravascular lithotripsy (IVL), drug coatedballoons, variable temperature balloons, and/or prosthetic heart valves.

Drug-Eluting Stents

Stents can be developed for specific patient groups depending onatherosclerosis characteristics and patient co-morbidities. Diabeticpatients may respond better to different drugs. In addition, determiningthe potential rejection or allergic reaction to a specific drug,polymer, or metal can be determined in advance if vessel wall biologyand patient response is understood in advance of the intervention. DESare generally made up three components: metallic stent, polymer anddrug. Any one of these variables can affect the long-term patency.

For patients with stent thrombosis elevation MI, perhaps DESs with BPare preferable. This has been further supported by the recently reportedBIOSTEMI trial showing superiority of ultra-thin BP sirolimus-elutingstent ORSIRO® over DP everolimus-eluting stent XIENCE® with respect toTLF at 1 year. For patients with high bleeding risk, BioFreedom™ orResolute Onyx™ with 1-month dual antiplatelet therapy (DAPT) have themost supportive data (Comparison of Contemporary Drug-eluting CoronaryStents—Is Any Stent Better than the Others? Available atwww.touchcardio.com/interventional-cardiology/journal-articles/comparison-of-contemporary-drug-eluting-coronary-stents-is-any-stent-better-than-the-others,Accessed May 7, 2021).

Patients with diabetes represent a challenging cohort. Most comparativetrials of different DESs have shown no difference in effect of stenttype between those with and without diabetes. In PLATINUM PLUS, therewas no difference the in risk of the primary endpoint between thosestented with PROMUS™ versus XIENCE™ (3.5% versus 3.5%, RR 1.00, 95% CI0.62-1.60). However, in the sub-group with diabetes, XIENCE was favored(7.8% versus 3.0%, RR 2.50, 95% CI 1.16-5.38, interaction p=0.05). Thisrelationship, however, was not seen in the 5-year follow-up data of thepreceding PLATINUM trial with a similar design. The comparison of BPDESs versus PP DESs in patients with diabetes was recently examined byBavishi et al., who included 5,190 patients from 11 RCTs in ameta-analysis, focusing on current-generation stents. After a meanfollow-up of 2.7 years, there were no differences in a range ofoutcomes, including target lesion revascularization (RR 1.02, 95% CI0.85-1.24, p=0.80) and stent thrombosis (1.66% versus 1.83%, RR 0.84,95% CI 0.54-1.31, p=0.45) between the two stent types. There was nodifference in this relationship between those patients with diabetestreated with and without insulin ((Comparison of ContemporaryDrug-eluting Coronary Stents— Is Any Stent Better than the Others?Available atwww.touchcardio.com/interventional-cardiology/journal-articles/comparison-of-contemporary-drug-eluting-coronary-stents-is-any-stent-better-than-the-others,Accessed May 7, 2021)).

Atherectomy Devices

Four different methods of atherectomy have been utilized for treatmentof femoropopliteal or small vessel infrapopliteal disease: plaqueexcision (directional) atherectomy, rotational atherectomy/aspiration,laser atheroablation, and orbital atherectomy.

Atherosclerotic plaque molecular signature, morphology proportions &volume can determine the ability of stents to fully expand and staypatent within the focal area which can affect long- and short-termoutcomes.

Understanding the lipid volume, matrix proportion, calcium extent, arc,thickness, volume, area and their impact on long term outcomes can helpdetermine if a patient will respond better acutely and if long termoutcomes/patency are improved when selecting different atherectomydevices for lesion preparation.

Intravascular Lithotripsy (IVL)

Atherosclerotic plaque molecular signature, morphology proportions &volume can determine the effectiveness of IVL within the focal lesionarea which can affect long- and short-term outcomes. The power and pulseof the lithotripsy can potentially be determined by the plaquemorphology.

Drug Coated Balloons

Target lesion revascularization rates in coronary and peripheralarterial disease can be affected by plaque morphology and/oratherosclerotic molecular signature. Different drug coated balloons canbe developed for specific patient groups depending on atherosclerosischaracteristics and patient co-morbidities. Patients with diabetescombined with different proportions of biological substances of theplaque can determine which drug balloon/excipient combo would be bestsuited for a particular patient. The type of drug (currently eitherpaclitaxel or sirolimus), the excipient and the timing of release(dosing) can be tailored depending on plaque morphology to extend targetlesion patency. Highly lipidic lesions like in-stent restenosis canaffect long term patency and warrant a patient specific drug. Highlycalcified lesions can require a different kind of drug coated balloon. Acombination of atherectomy, plus a specific drug coated balloon can beselected based on molecular signature of the plaque.

Variable Temperature Balloons

The atherosclerotic lesion molecular signature can help determine if apatient is not a good candidate (would not respond well) for drug coatedballoon or drug eluting stent and requires an alternative interventionaltherapy. The patient may have co-morbidities or allergies to certaindrugs requiring a different therapeutic approach. This can avoidcatastrophic acute reactions and long-term implications of sometimespermanent implants. The use of a “hot balloon” or a “cold balloon” maybe warranted for certain lesion morphology characteristics.

Cryoplasty combines the dilatation force of angioplasty with thesimultaneous delivery of cold thermal energy to the arterial wall. Bothmechanisms are achieved simultaneously by filling the angioplastycatheter with nitrous oxide instead of the usual contrastsaline/solution mixture. Cryotherapy has been proven to biologicallyalter the behavior of arterial cellular components in a benign healingprocess (The Next-Generation PolarCath™ System Available atevtoday.com/articles/2018-jan-supplement/the-next-generation-polarcath-system,Accessed May 10, 2021).

Several scientific studies have demonstrated that this cooling processwithin the vessel results in: weakening of the plaque, promoting uniformdilation and reducing vessel trauma; alteration of elastin fibers toreduce vessel wall recoil, while collagen fibers remain undisturbed andcapable of maintaining architectural integrity; induction of smoothmuscle apoptosis, which is associated with reduced neointimal formationand, subsequently, less restenosis ((The Next-Generation PolarCathSystem Available atevtoday.com/articles/2018-jan-supplement/the-next-generation-polarcath-system,Accessed May 10, 2021)).

So called Hot Balloons are currently in development and may alter themorphology and fibrous cap thickness while reducing neointimalhyperplasia seen with standard angioplasty balloons.

Prosthetic Heart Valves

Understanding the molecular signature of heart valve disease phenotypescan help determine which drugs can arrest the disease and potentiallyreverse it before progressing to an irreparable state. In addition, thepathology of patient specific valve disease can determine the long-termefficacy and patient response to a particular prosthetic heart valve(TAVR: self-expanding, balloon expandable or different surgicallyimplanted valves.)

Heart valves are complex tri-layered structures that ensure theunidirectional flow of blood. Scientists are actively investigating howcharacteristics of the two major cell types, valvular endothelial cells(VECs) and valvular interstitial cells (VICs), and their mechanicalrelationships with the valvular extracellular matrix promote structuralintegrity and age-related remodeling. Abnormal changes in VECs, VICs,and the extracellular matrix at the molecular level lead to gross tissuemalformations and dysfunction. Improving our understanding of heartvalve biology, the impact of cardiovascular drugs, and remodelingchanges will be critical to the development of novel therapies for heartvalve diseases (Xu, S. and K. J. Grande-Allen (2010). “The role of cellbiology and leaflet remodeling in the progression of heart valvedisease.” Methodist Debakey Cardiovasc J 6(1): 2-7).

The clinical and pathological features of the most frequent intrinsicstructural diseases that affect the heart valves are well established,but heart valve disease mechanisms are poorly understood, and effectivetreatment options are evolving. Major advances in the understanding ofthe structure, function and biology of native valves and thepathobiology, biomaterials and biomedical engineering, and the clinicalmanagement of valvular heart disease have occurred over the past severaldecades (Schoen, F. J. (2018). “Morphology, ClinicopathologicCorrelations, and Mechanisms in Heart Valve Health and Disease.”Cardiovasc Eng Technol 9(2): 126-140).

Procedural interventions in CAD include coronary artery bypass grafts(CABG), percutaneous coronary intervention (PCI, e.g., balloonangioplasty with or without stent placement). Also of relevance areprocedures for valve replacement or repair including transcatheteraortic valve replacement (TAVR), due to the need for coronary arteryassessment in the pre-procedure work-up.

Optimal Medical Therapy (OMT)

Most subjects on statins are prescribed a relatively low dose, but asthere are indications of plaque requiring more intensity, variousapproaches exist. One approach is to increase the dose, for example,high-dose atorvastatin is often prescribed for subjects withhypercholesterolemia. There is a growing consensus thathypertriglyceridemia vs. hypercholesterolemia differs (Le, N. A. and M.F. Walter, The role of hypertriglyceridemia in atherosclerosis. CurrAtheroscler Rep, 2007. 9(2): p. 110-5), with at least one recent drug(Vascepa®) capturing current attention. For subjects withhypertriglyceridemia, improved outcomes have been reported in trialssuch as the Reduction of Cardiovascular Events with EPA—InterventionTrial (REDUCE-IT) trial (Bhatt et al., REDUCE-IT USA: Results From the3146 Patients Randomized in the United States. Circulation, 2020.141(5): p. 367-375; Bhatt et al., Cardiovascular Risk Reduction withkosapent Ethyl for Hypertriglyceridemia. N Engl J Med, 2019. 380(1): p.11-22; Bhatt et al., Reduction in First and Total Ischemic Events Withkosapent Ethyl Across Baseline Triglyceride Tertiles. J Am Coll Cardiol,2019. 74(8): p. 1159-1161; Bhatt, D. L., Reduce-It. Eur Heart J, 2019.40(15): p. 1174-1175; Bhatt et al., Effects of kosapent Ethyl on TotalIschemic Events: From REDUCE-IT. J Am Coll Cardiol, 2019. 73(22): p.2791-2802; Boden et al., Profound reductions in first and totalcardiovascular events with icosapent ethyl in the REDUCE-IT trial: whythese results usher in a new era in dyslipidaemia therapeutics. EurHeart J, 2019). Detailed quantitative studies have yet to be done todetermine how IPE affects tissues in the vessel wall because it has notbeen previously possible to quantitatively assess changes in plaquemorphology non-invasively.

Other Emerging Drug Classes

Triggers of innate immunity and regulation of intracellular signaltransduction suggests novel targets for therapeutic treatment, includingthe inhibition of the pro-inflammatory cytokines induced on dangersignaling (Zimmer et al., Danger signaling in atherosclerosis. Circ Res,2015. 116(2): p. 323-40). As an example, stimulating immune tolerancewith increased Treg activity is being explored (Herbin et al.,Regulatory T-cell response to apolipoprotein B100-derived peptidesreduces the development and progression of atherosclerosis in mice.Arterioscler Thromb Vasc Biol, 2012. 32(3): p. 605-12). As anotherexample, clearing chylomicron remnants (large triglyceride-richlipoproteins) (Rahmany, S. and I. halal, Biochemistry, Chylomicron, inStatPearls. 2020: Treasure Island (FL)) is atheroprotective sincechylomicron particles and the triglyceride-rich particles are directlyand indirectly implicated in plaque development (Tomkin, G. H. and D.Owens, The chylomicron: relationship to atherosclerosis. Int J Vasc Med,2012. 2012: p. 784536).

Drug candidates in other therapeutic areas, such as immuno-modulators incancer can have side effects where atherosclerosis is aggravated, due toactivation of T-cells in the plaques that can result in plaque rupture,but there are no accurate methods to track these effects. There is awidely recognized need for effective markers during drug development foratherosclerosis and even unrelated diseases as well as companiondiagnostics post-marketing.

VI. Examples of Applications

Clinical Decision Support Systems

The present disclosure can be used as a clinical decision supportsystem. The invention supports clinical decision making by informing theclinician on what the likely effect would be for different possibletherapies, and also provides tools to help discuss these options withthe patient. The disclosure provides a recommendation based on thestatistical significance of the likely improvement, and can compareacross potential recommendations to identify the one that has beenconsidered which exceeds others in the degree of improvement provided.This recommendation can be understood as determining a clinical action,or informing a decision that leads to a clinical action.

Such recommendations and actions that proceed from use of the presentlydisclosed methods and systems allow therapy to be tailored to theindividual rather than be based only on population statistics.Presently, clinical guidelines have not been able to use such diagnosticspecificity because there have been no means to do so. Individuals havedifferent genetic pre-disposition, environmental exposures, anddiffering lifestyle habits. Both modifiable and non-modifiable riskfactors influence what is best for that patient. The in silico systemsbiology models described herein provide a description of the disease,and a way to process and calibrate it for individual patients. This thenenables the actual expected effect of therapies to be evaluated morespecifically than previously possible. The benefit is that rather thanreferring to the population as a whole or at best sub-populations, thatactual molecular level effects may be considered.

This has been widely understood in cancer treatment and is increasinglythe norm. However, whereas cancer is generally informed by moleculardiagnostics run on biopsied tumor tissue, it is not possible to biopsyatherosclerotic plaque tissues because it can cause a disruption, whichis not desired. As a result, the computer-based systems describedherein, which utilize advanced techniques, including forms of artificialintelligence, can extend what the clinicians would otherwise be able todo by themselves. The features of the tissues are generally of toocomplex a nature as to be easily interested by a human observer but thepresent invention analyses data at a far more granular level. To makesuch a decision support system practical is a mix of mathematicalformulations, knowledge representation, and architecture in terms ofuser interfaces, reporting systems, and backbone of computation, all asdescribed herein.

The utility of any diagnostic system must address what can be done withthe information. Presently numerous powerful therapies exist, bothprocedural, pharmaceutical, or combinations such as drug eluting stents.By evaluating the individualized response to these therapies, thecurrent systems and methods make diagnostics actionable by identifyingthe degree of improvement and annotates that improvement level with thestatistical significance of its computation. These recommendations canbe presented, for example, on screen-based user interfaces or inprintable PDF forms that may be used in communicating among groups ofclinicians or with patients.

Identification of Likely Responses at an Individual Patient Level

Provided herein are methods and systems for identifying a likelyresponse, at an individual patient level, for a potential therapeuticagent. More specifically, an in silico systems biology model isgenerated, trained, and updated to create a calibrated model, asdescribed herein. Then the calibrated model is updated withpatient-specific information (e.g., virtual ′omics or from histologicalanalysis obtained from actual tissue and/or blood specimens), to createthe baseline condition. The in silico systems biology model representingthe baseline condition is then further updated to simulate one or morepotential therapies based on the mechanism of action for each therapy toarrive at various in silico systems biology model representations ofvarious simulated conditions for each potential therapy. Based on theresults, the patient is provided with a recommendation, e.g., in theform of a report, of a suitable therapy or treatment regimen. Theresulting absolute pathology as well as the relative improvement in thepathology can be quantified and expressed as a likely response for eachsimulated therapy.

Quantification of Actual Responses at an Individual Patient Level

Also provided herein are methods and systems for quantifying an actualresponse, at an individual patient level, for a potential therapeuticagent. More specifically, an in silico systems biology model isgenerated, trained, and updated to create the calibrated model, asdescribed herein. Then the calibrated model is updated withpatient-specific information (e.g., virtual ′omics or from histologicalanalysis obtained from actual tissue and/or blood specimens), to createthe baseline condition. The in silico systems biology model representingthe baseline condition is then further updated to simulate eachpotential therapy based on the mechanism of action for each therapy toarrive at various in silico systems biology model representations ofvarious simulated conditions for each potential therapy. Based on theresults, the patient is provided with a recommendation of a suitabletherapy or treatment regimen.

After the patient has been on the recommended treatment regimen for atime sufficient to elicit a therapeutic response, the in silico systemsbiology model (i.e., a calibrated model that has not been updated withnew patient-specific information) is updated with new patient-specificinformation (e.g., new virtual ′omics data), to create a model thatrepresents a simulation of the effect of the recommend therapy(after-treatment simulation).

The baseline condition is compared to the after-treatment simulation. Ifthere has been an actual improvement in the pathology, that resultprovides an indication that the patient improved under the treatment,even if the specific changes to the protein levels were not exactly asoriginally simulated. Further, if the specific changes to the proteinlevels were approximately as simulated, then one can further determinethat the treatment caused the improvement and the method can beconsidered a surrogate end-point for treatment effect. In other words,in some embodiments, the simulations need to be only approximatelycorrect to provide the intended utility in clinical practice.

Quantification of Actual Responses at a Cohort Level

Also provided herein are methods and systems of determining the actualresponses to a specific treatment at a cohort level of patients or testsubjects.

For example, an in silico systems model can be built. More specifically,an in silico systems biology model can be generated, trained, andupdated to create the calibrated system, as described here. Then, foreach patient or test subject in a cohort, information from each patient(e.g., virtual ′omics or from histological analysis obtained from actualtissue and/or blood specimens) is used to update the model for eachpatient/test subject to form the baseline condition. For eachpatient/test subject in the cohort and for each therapy to be simulated,the calibrated model is perturbed based on the mechanism of action forthe therapy to arrive at a simulated condition.

After an interval where each patient/test subject in the cohort hasreceived the (adjusted) recommended treatment, e.g., after a timesufficient to elicit a therapeutic response, the in silico systemsbiology model, i.e., a calibrated model that has not been updated withnew patient-specific information, is updated with new patient-specificinformation (e.g., new virtual ′omics or new histological analysisobtained from actual tissue and/or blood specimens), to create a modelthat represents an after-treatment simulation. If there has been anactual improvement in the pathology across the cohort of patients, onecan conclude that the patients improved under the treatment, even if thespecific changes to the protein levels were not exactly as simulated.Further, if the specific changes to the protein levels wereapproximately as simulated, then it can further be said that thetreatment caused the improvement and the method may be considered to bea surrogate end point for treatment effect. This can be performed in thecontext of an observational study, a randomized clinical trial, or otherstudy designs.

Detecting Contraindications at an Individual Patient Level

Also provided herein are methods and systems wherein after the simulatedconditions are generated for each potential therapy, contra-indicationsat the individual patient level are detected.

For example, provided herein are methods for identifying a likelyresponse, at an individual patient level, for a potential therapeuticagent. More specifically, an in silico systems biology model isgenerated, trained, and updated to create the calibrated system, asdescribed above. Then the calibrated model is updated withpatient-specific information (e.g., virtual ′omics or from histologicalanalysis obtained from actual tissue and/or blood specimens), to createthe baseline condition. The in silico systems biology model representingthe baseline condition, also as described above, is then further updatedto simulate each potential therapy based on the mechanism of action foreach treatment to arrive at various in silico systems biology modelrepresenting various simulated condition for each potential treatment.Deleterious side effects in the simulated condition are determined bylooking at how molecules are perturbed in the model. That is, even ifthere is an apparent improvement in the condition with respect to thepathology, there may be inadvertent other effects that are worse for thepatient than the intended improvement.

Once determined, those other effects can also be provided to thepatient, e.g., in a report.

Identification of Likely Adverse Reactions, Current Actual Toxicity, orLikely Future Negative Reactions, at an Individual Patient Level

Also provided herein are methods and systems, wherein after thesimulated conditions are generated for each potential treatment, likelyadverse reactions, current actual toxicity, or likely future negativereactions, at an individual patient level are identified.

For example, provided herein are methods and systems for identifying alikely response, at an individual patient level, for a potentialtherapeutic agent. More specifically, an in silico systems biology modelis generated, trained, and updated to create the calibrated model, asdescribed herein. Then the calibrated model is updated withpatient-specific information (e.g., virtual ′omics or from histologicalanalysis obtained from actual tissue and/or blood specimens), to createthe baseline condition. The in silico systems biology model representingthe baseline condition, also as described herein, is then furtherupdated to simulate each potential therapy based on the mechanism ofaction for each therapy to arrive at various in silico systems biologymodel representations of various simulated conditions for each potentialtherapy.

Deleterious side effects are determined (adverse reaction) in thesimulated condition, that is, even if there is an apparent improvementin the condition with respect to the pathology, there may be inadvertentother effects that are worse for the patient than the intendedimprovement. One can use this information to modify the therapyrecommendations, that is, for example, one may downgrade arecommendation for treatments that improve the pathology, but also haveone or more adverse reactions.

After an interval where the patient(s) has received the (adjusted)recommended treatment, e.g., after a time sufficient to elicit atherapeutic response, the in silico systems biology model (i.e., onethat has not been updated with new patient-specific information) isupdated with new patient-specific information, e.g., information eitherobtained through collection of tissue and/or blood specimens from thepatient using transcriptomics and/or proteomics and/or metabolomics, orfrom non-invasive prediction (virtual ′omics)), to create a model thatrepresents an after-treatment simulation.

If there has been an actual improvement in the pathology, one canconclude that the patient or patients improved under the therapy, evenif the specific changes to the protein levels were not exactly assimulated. Further, if the specific changes to the protein levels wereapproximately as simulated, then one can further determine that thetherapy caused the improvement and the method can be considered to be asurrogate end point for a treatment effect.

If there has been an adverse effect, one can determine that the patientfailed to improve under the treatment, even if the specific changes tothe protein levels were not exactly as simulated.

In some instances, the in silico model can be rebuilt (i.e., step one)with additional information regarding adverse events. All subsequentsteps can then be repeated to determine additional improvement, adverseeffects, or both for modifying treatments or for conducting dynamic,combination, multi-stage, or adaptive clinical trial designs orindividual patient management.

Screening Tools for Clinical Trial Enrichment to “Select In” Cases thatIncrease the Statistical Power of a Clinical Trial

Also provided herein are methods and systems for creating and usingscreening tools for clinical trials to determine “select in” cases. Morespecifically, an in silico systems biology model is generated, trained,and updated to create the calibrated system, as described herein. Thenthe calibrated model is updated with patient-specific information (e.g.,virtual ′omics or from histological analysis obtained from actual tissueand/or blood specimens), to create the baseline condition. The in silicosystems biology model representing the baseline condition, also asdescribed herein, is then further updated to simulate each potentialtreatment based on the mechanism of action for each treatment to arriveat various in silico systems biology model representing varioussimulated condition for each potential treatment. The resultingpathology as well as the relative improvement in the pathology isquantified and expressed as a likely response for each simulatedtreatment.

If the likely improvement of the patient is above an inclusion criteriathreshold, one would select the patient for the clinical trial.Otherwise, one would not select the patient for the clinical trial, ifthere are no other exclusion or inclusion criteria issues.

Screening Tools for Clinical Trial Enrichment to “Select Out” Cases thatDecrease the Statistical Power of a Clinical Trial

Also provided herein are methods and systems for creating and usingscreening tools for clinical trials to determine “select out” cases.More specifically, an in silico systems biology model is generated,trained, and calibrated to create the calibrated system, as describedabove. Then the calibrated model is updated with patient-specificinformation (e.g., virtual ′omics or from histological analysis obtainedfrom actual tissue and/or blood specimens), to create the baselinecondition. The in silico systems biology model representing the baselinecondition, also as described herein, is then further updated to simulateeach potential treatment based on the mechanism of action for eachtreatment to arrive at various in silico systems biology modelrepresenting various simulated condition for each potential treatment.Any deleterious side effects (adverse reaction) in the simulatedcondition are flagged, that is, even if there is an apparent improvementin the condition with respect to the pathology, there may be aninadvertent other effect that is worse for the patient than the intendedimprovement.

If the adverse reaction of the patient is above an exclusion criteriathreshold, then one would not select the patient for the clinical trial.Otherwise, one would select the patient for the clinical trial, if thereare no other exclusion or inclusion criteria issues.

EXAMPLES

The invention is further described in the following examples, which donot limit the scope of the invention described in the claims.

Example 1: Creation of an in Silico Systems Biology Model

Methods

Cohort Assembly and Proteomic Processing

A total of 22 male patients on statin therapy undergoingstroke-preventive carotid endarterectomy (CEA) for high-grade (>50%NASCET (Golriz Khatami, S. et al. Using predictive machine learningmodels for drug response simulation by calibrating patient-specificpathway signatures. npj Systems Biology and Applications 7, 1-9 (2021)))stenosis were prospectively enrolled to represent the differences inprotein levels between unstable and stable atherosclerosis, yielding 18patients with data from CTA, histology, and plaque proteomics forcomplete characterization (comprising three spatial scales) (see FIGS.3A-3F).

Study Cohort Demographics are Summarized in

Table 3, below. Briefly, CEAs were collected at surgery and retainedwithin a biobank, with details of sample collection and processingpreviously described.^(11,12) All samples were collected with informedconsent from patients and the study was approved by the Ethical ReviewBoard. Continuous variables are presented as medians (inter-quartilerange). No variable was found to be significantly different betweenstable and unstable phenotypes.

Demographic variables were summarized to characterize the cohort andidentify significantly different values across plaque subgroups.Categoric variables with less than 25% missing data were tabulated withfractions and significance analysed with Fisher Exact test. Continuousvariables were tabulated as medians with inter-quartile range andsignificance analysed by Wilcoxon non-parametric test (using aconfidence level of p=0.05).

TABLE 3 Study Cohort Demographics Categoric Stable Unstable p ContinuousStable Unstable p Male 100% 100% 1.00 Age 66.97 72.63 0.32 (8/8) (10/10)(7.76) (11.52) Previous MI  50%  20% 0.32 High-sensitivity CRP 1.00 2.700.10 (4/8) (2/10) (mg/l) (1.19) (2.50) Angina Pectoris  12%  10% 1.00S-Cholesterol (mmol/l) 3.40 4.00 0.15 (1/8) (1/10) (0.70) (0.80)Hypertension  88%  80% 1.00 Triglycerides (mmol/l) 1.00 1.50 0.17 (7/8)(8/10) (0.31) (1.10) PVD  12%  10% 1.00 Hemoglobin (g/dl) 151.00 141.000.23 (1/8) (1/10) (14.50) (12.00) Smoker  12%  22% 1.00 Diastolic BP62.50 76.00 0.34 (1/8) (2/9) (2.50) (11.00) % Stenosis by U/S 89.5075.00 0.55 (9.50) (25.00) Hemoglobin A1C 3.90 4.40 0.55 (mmol/mol)(0.50) (1.30) LPK 7.80 6.70 0.63 (1.35) (1.40) EVF 41.50 41.00 0.66(41.79) (4.00) Fibrinogen 3.90 3.60 0.66 (0.90) (0.47) LDL (mmol/) 1.601.95 0.72 (0.65) (0.80) S-Creatinine (mg/dl) 81.50 85.00 0.77 (32.75)(21.00) BMI 25.40 25.72 0.83 (3.11) (3.89) HDL (mmol/l) 1.20 1.20 0.91(0.25) (0.40) Erythrocyte count 4.90 4.65 0.94 (0.45) (0.80) eGFR 72.0065.00 0.96 (18.50) (23.00) Systolic BP 130.00 132.50 1.00 (5.00) (13.75)

Excised plaques were divided transversally at the most stenotic part;the proximal half used for protein analysis and the distal half fixed in4% formaldehyde and prepared for histology. Histological analysis wasperformed on Masson-Tri-Chrome stained sections to assess presence ofinstability features such as lipid-rich necrotic core (LRNC),intra-plaque haemorrhage (IPH), fibrous cap thickness and integrity, andother factors according to the Virmani classification (Barrett, T. J.Macrophages in Atherosclerosis Regression. Arteriosclerosis, thrombosis,and vascular biology 40, 20-33, doi:10.1161/ATVBAHA.119.312802 (2020))categorizing symptomatic and asymptomatic patients based on plaquestability (minimal, stable, or unstable) and resulting in 18 patientsappropriately matched with respect to symptomatology and plaquemorphology features. We further characterized the patients utilizinganalyses from CTA by ElucidVivo (Boston, Mass. USA) for plaquemorphology comprising structural anatomy and tissue characteristics aswell as non-invasive plaque stability classification (see, e.g., FIGS.3A-3F). These methods can elucidate prevalent biological processesrelevant for plaque instability as previously described (Kalluri. &Weinberg, The basics of epithelial-mesenchymal transition. J Clin Invest119, 1420-1428, doi:10.1172/JCI39104 (2009); Kovacic et al.,Epithelial-to-mesenchymal and endothelial-to-mesenchymal transition:from cardiovascular development to disease. Circulation 125, 1795-1808,doi:10.1161/CIRCULATIONAHA.111.040352 (2012)).

LC-MS/MS Analysis and Protein Identification

Using methods previously described (Evrard, S. M. et al. Corrigendum:Endothelial to mesenchymal transition is common in atheroscleroticlesions and is associated with plaque instability. Nat Commun 8, 14710,doi:10.1038/ncomms14710 (2017)) plaques from selected patients wereprocessed for proteomic analysis. Briefly, 4 mm thick sections wereretrieved from the proximal half of the lesion, one from the peripheralend and one from the central core. Proteomic processing was performedusing high-resolution isoelectric focusing (HiRIEF (Newby, A. C. et al.Vulnerable atherosclerotic plaque metalloproteinases and foam cellphenotypes. Thrombosis and haemostasis 101, 1006-1011 (2009))) withmedian normalization of ratios on the peptide spectrum match (PSM)level. FTMS master scans were followed by data-dependent MS/MS. Spectrawere searched using MSGF+ (v10072) (Bittner et al., P6164 High level ofEPA is associated with lower perivascular coronary attenuation asmeasured by coronary CTA. European heart journal 40, ehz746. 0770(2019)) and Percolator (v2.08) (Antonopoulos, A. S. et al. Detectinghuman coronary inflammation by imaging perivascular fat. Sciencetranslational medicine 9, doi:10.1126/scitranslmed.aa12658 (2017)),where search results were grouped for Percolator target/decoy analysis.PSMs found at 1% PSM- and peptide-level FDR (false discovery rate) wereused to infer gene identities, and median normalization of ratios on thePSM level was performed. Protein level FDRs were calculated using thepicked-FDR method (Raj sheker, S. et al. Crosstalk between perivascularadipose tissue and blood vessels. Curr Opin Pharmacol 10, 191-196,doi:10.1016/j.coph.2009.11.005 (2010)).

Cell Network Pathway Selection

A systems biology model was created from a combination of proteomicpathways based on the differences in plaque stability, which representedlate-stage disease, augmented with literature-based and data baseretrieval, e.g., from the Kyoto Encyclopedia of Genes and Genomes (KEGG)database, to ensure coverage of earlier stages of atherogenesis.Keywords were used to search the KEGG database (see, e.g., Table 4below).

KEGG is a database resource for understanding high-level functions andutilities of the biological system, such as the cell, the organism, andthe ecosystem, from genomic and molecular-level information. It is acomputer representation of the biological system, consisting ofmolecular building blocks of genes and proteins (genomic information)and chemical substances (chemical information) that are integrated withthe knowledge on molecular wiring diagrams of interaction, reaction, andrelation networks (systems information). It also contains disease anddrug information (health information) as perturbations to the biologicalsystem. In KEGG, reference pathway maps of molecularinteraction/reaction network diagrams are represented in terms of theKEGG Orthology (KO) groups, so that experimental evidence in specificorganisms can be generalized to other organisms through genomicinformation. In other words, maps (such as the ones referred to inTables 5 and 6 below) are reference maps and are noted with a “mapxxxxx”identification number. These maps can then be generalized to Homosapiens (i.e., humans) and are noted with a “hsaxxxxx” identificationnumber. For example, map05417 refers to the reference pathway for Lipidand Atherosclerosis, and HSA05417 refers to the Lipid andAtherosclerosis pathway in Homo sapiens.

TABLE 4 KEGG Pathway Database Search Terms Used for Identification ofPathways Drawn from Literature Reviews aaa efferocytosis jak2proteoglycan abca1 egfr klf2 psoriasis adaptive endoplasmic ldl resolvinadipose endothelial ldlr sirolimus adventitia endothelin leukin smc ampkenos leukocyte statin angiogenesis epithelial lipidemia stretch apobe-selectin lipoprotein subtilisin %2Fkexin apoc Everolimus Long-termtcell apoe Extracellular + lymphocyte Terpenoid + matrix backbone +biosynthesis asxl1 Fat + digestion + macrophage TET2 and + absorptionathero fatty + acid mast TGF atheroma fibroblast MCP1 tgfaatherosclerosis fibronectin metalloproteinase TGFB blvrb flk1 metforminthrombin Calcification foam MMP thrombus Canakinumab glycocalyx MMP2tie2 Glycolysis + %2F + monocyte timp2 Gluconeogenesis catecholamineshdl mrna tp53 cd4 hematopoietic necrosis treg cell + cycle hscrpneutrophil triglyceride Cellular + hypertension nfkb triglyceridemiasenescence cetp icam1 N-Glycan + u937 biosynthesis cholesterolemiaIcosapent nicotine vasa + vasorum chylomicron ifn nitric + oxidevasodilation cigarette il17 oxidative + stress vcam1 colchicine il1b padvegf collagen il6 pcsk vldl coronary il8 peripheral vsmc %2C + atheroC-reactive + immune Phosphatidylinositol protein CRP immunologyPrimary + bile + acid + biosynthesis cytokine inflammation progenitordnmt innate proprotein + convertase dnmt3a insulin proresolving ecmintima prostacyclin

Selected pathways were assigned according to their applicability to fourprimary cell types: endothelial cells (ECs), vascular smooth musclecells (VSMCs), macrophages, and lymphocytes (Table 5). In Table 5, a “1”is placed to signify that the pathway has a more than trivialparticipation in the given cell type. In general, pathways were deemedeither fully included or fully excluded relative to a cell type (Table6). In Table 6, the table comprises pathways which are generally commonto mammalian cells of many types, including those identified. Somepathways contained cell-type specific portions. In such cases, pathwayswere split prior to inclusion.

TABLE 5 Relevance of Selected Proteomic Pathways to Four Cell Types ecvsmc mac lymphocyte relevance relevance relevance relevance Entry Name 1map04270 Vascular smooth muscle contraction 1 map04370 VEGF signalingpathway 1 map04380 Osteoclast differentiation 1 map04613 Neutrophilextracellular trap formation 1 map04650 Natural killer cell mediatedcytotoxicity 1 map04658 Th1 and Th2 cell differentiation 1 map04659 Th17cell differentiation 1 map04660 T cell receptor signaling pathway 1map04662 B cell receptor signaling pathway 1 map04664 Fc epsilon RIsignaling pathway 1 map04666 Fc gamma R-mediated phagocytosis 1 map04915Estrogen signaling pathway 1 map04931 Insulin resistance 1 map04940 TypeI diabetes mellitus 1 map05418 Fluid shear stress and atherosclerosis 11 map00510 N-Glycan biosynthesis 1 1 map01523 Antifolate resistance 1 1map03320 PPAR signaling pathway 1 1 map04062 Chemokine signaling pathway1 1 map04510 Focal adhesion 1 1 map04520 Adherens junction 1 1 map04530Tight junction 1 1 map04540 Gap junction 1 1 map04610 Complement andcoagulation cascades 1 1 map04611 Platelet activation 1 1 map04612Antigen processing and presentation 1 1 map04622 RIG-I-like receptorsignaling pathway 1 1 map04623 Cytosolic DNA-sensing pathway 1 1map04625 C-type lectin receptor signaling pathway 1 1 map04630 JAK-STATsignaling pathway 1 1 map04640 Hematopoietic cell lineage 1 1 map04657IL-17 signaling pathway 1 1 map04750 Inflammatory mediator regulation ofTRP channels 1 1 map04810 Regulation of actin cytoskeleton 1 1 map04920Adipocytokine signaling pathway 1 1 map04923 Regulation of lipolysis inadipocytes 1 1 map04979 Cholesterol metabolism 1 1 1 map00531Glycosaminoglycan degradation 1 1 1 map01040 Biosynthesis of unsaturatedfatty acids 1 1 1 map04014 Ras signaling pathway 1 1 1 map04371 Apelinsignaling pathway 1 1 1 map04514 Cell adhesion molecules 1 1 1 map04621NOD-like receptor signaling pathway 1 1 1 map04670 Leukocytetransendothelial migration 1 1 1 map04911 Insulin secretion 1 1 1map04922 Glucagon signaling pathway 1 1 1 map04933 AGE-RAGE signalingpathway in diabetic complications 1 1 1 map04935 Growth hormonesynthesis, secretion and action

TABLE 6 Selected Proteomic Pathways Included in all Four Cell Types ecvsmc mac lymphocyte relevance relevance relevance relevance Entry Name 11 1 1 ap07042 Antineoplastics - agents from natural products 1 1 1 1map00010 Glycolysis/ Gluconeogenesis 1 1 1 1 map00020 Citrate cycle (TCAcycle) 1 1 1 1 map00030 Pentose phosphate pathway 1 1 1 1 map00520 Aminosugar and nucleotide sugar metabolism 1 1 1 1 map00532 Glycosaminoglycanbiosynthesis - chondroitin sulfate/dermatan sulfate 1 1 1 1 map00534Glycosaminoglycan biosynthesis - heparan sulfate/heparin 1 1 1 1map00562 Inositol phosphate metabolism 1 1 1 1 map00590 Arachidonic acidmetabolism 1 1 1 1 map00630 Glyoxylate and dicarboxylate metabolism 1 11 1 map00910 Nitrogen metabolism 1 1 1 1 map00920 Sulfur metabolism 1 11 1 map01100 Metabolic pathways 1 1 1 1 map01200 Carbon metabolism 1 1 11 map01212 Fatty acid metabolism 1 1 1 1 map01240 Biosynthesis ofcofactors 1 1 1 1 map02010 ABC transporters 1 1 1 1 map04010 MAPKsignaling pathway 1 1 1 1 map04012 ErbB signaling pathway 1 1 1 1map04015 Rap1 signaling pathway 1 1 1 1 map04020 Calcium signalingpathway 1 1 1 1 map04022 cGMP-PKG signaling pathway 1 1 1 1 map04024cAMP signaling pathway 1 1 1 1 map04060 Cytokine-cytokine receptorinteraction 1 1 1 1 map04064 NF-kappa B signaling pathway 1 1 1 1map04066 HIF-1 signaling pathway 1 1 1 1 map04068 FoxO signaling pathway1 1 1 1 map04070 Phosphatidylinositol signaling system 1 1 1 1 map04071Sphingolipid signaling pathway 1 1 1 1 map04072 Phospholipase Dsignaling pathway 1 1 1 1 map04080 Neuroactive ligand-receptorinteraction 1 1 1 1 map04110 Cell cycle 1 1 1 1 map04115 p53 signalingpathway 1 1 1 1 map04120 Ubiquitin mediated proteolysis 1 1 1 1 map04137Mitophagy - animal 1 1 1 1 map04141 Protein processing in endoplasmicreticulum 1 1 1 1 map04142 Lysosome 1 1 1 1 map04144 Endocytosis 1 1 1 1map04145 Phagosome 1 1 1 1 map04150 mTOR signaling pathway 1 1 1 1map04151 PI3K-Akt signaling pathway 1 1 1 1 map04152 AMPK signalingpathway 1 1 1 1 map04210 Apoptosis 1 1 1 1 map04211 Longevity regulatingpathway 1 1 1 1 map04216 Ferroptosis 1 1 1 1 map04217 Necroptosis 1 1 11 map04218 Cellular senescence 1 1 1 1 map04310 Wnt signaling pathway 11 1 1 map04330 Notch signaling pathway 1 1 1 1 map04350 TGF-betasignaling pathway 1 1 1 1 map04390 Hippo signaling pathway 1 1 1 1map04512 ECM-receptor interaction 1 1 1 1 map04550 Signaling pathwaysregulating pluripotency of stem cells 1 1 1 1 map04614 Renin-angiotensinsystem 1 1 1 1 map04620 Toll-like receptor signaling pathway 1 1 1 1map04668 TNF signaling pathway 1 1 1 1 map04710 Circadian rhythm 1 1 1 1map04722 Neurotrophin signaling pathway 1 1 1 1 map04910 Insulinsignaling pathway 1 1 1 1 map04912 GnRH signaling pathway 1 1 1 1map04919 Thyroid hormone signaling pathway 1 1 1 1 map04924 Reninsecretion 1 1 1 1 map07046 Immunosuppressive agents 1 1 1 1 map07051Antidiabetics

Table 7 below lists pathways important for lipid lowering. A high numberlisted in the “lipid significance” column means that the pathway ishighly significant, a low number means low significance.

TABLE 7 Top Lipid-related Pathways Lipid KEGG Map Significance Entry No.Pathway Name 120 map05417 Lipid and atherosclerosis 64 map04979Cholesterol metabolism 64 map04923 Regulation of lipolysis in adipocytes48 map04060 Cytokine-cytokine receptor interaction 48 map04064 NF-kappaB signaling pathway 48 map04668 TNF signaling pathway 48 map04910Insulin signaling pathway 38 map05418 Fluid shear stress andatherosclerosis 36 map04670 Leukocyte transendothelial migration 36map04911 Insulin secretion 36 map04933 AGE-RAGE signaling pathway indiabetic complications 32 map04010 MAPK signaling pathway 32 map04145Phagosome 32 map04614 Renin-angiotensin system 32 map07046Immunosuppressive agents 24 map04657 IL-17 signaling pathway 20 map04750Inflammatory mediator regulation of TRP channels 16 map00010 Glycolysis/Gluconeogenesis 16 map04141 Protein processing in endoplasmic reticulum16 map04150 mTOR signaling pathway 16 map04152 AMPK signaling pathway 16map04211 Longevity regulating pathway 16 map07051 Antidiabetics 12map04062 Chemokine signaling pathway 12 map04920 Adipocytokine signalingpathway 12 map04371 Apelin signaling pathway 10 map04931 Insulinresistance

Table 8 below lists pathways important for anti-inflammation. A highnumber listed in the “inflammation significance” column means that thepathway is highly significant, a low number means low significance.

TABLE 8 Top Inflammation-related Pathways Inflammation KEGG MapSignificance Entry No. Pathway Name 96 map04060 Cytokine-cytokinereceptor interaction 96 map04064 NF-kappa B signaling pathway 96map04668 TNF signaling pathway 80 map04910 Insulin signaling pathway 72map05417 Lipid and atherosclerosis 72 map04670 Leukocytetransendothelial migration 64 map04010 MAPK signaling pathway 64map04145 Phagosome 64 map04614 Renin-angiotensin system 64 map07046Immunosuppressive agents 60 map04911 Insulin secretion 60 map04933AGE-RAGE signaling pathway in diabetic complications 48 map04657 IL-17signaling pathway 40 map04750 Inflammatory mediator regulation of TRPchannels 24 map04062 Chemokine signaling pathway 24 map04920Adipocytokine signaling pathway 20 map05418 Fluid shear stress andatherosclerosis 16 map00010 Glycolysis/ Gluconeogenesis 16 map04141Protein processing in endoplasmic reticulum 16 map04150 mTOR signalingpathway 16 map04152 AMPK signaling pathway 16 map04211 Longevityregulating pathway 16 map07051 Antidiabetics 16 map04931 Insulinresistance 16 map04979 Cholesterol metabolism 16 map04923 Regulation oflipolysis in adipocytes 16 map04660 T cell receptor signaling pathway 12map04371 Apelin signaling pathway 12 map04658 Th1 and Th2 celldifferentiation 12 map04659 Th17 cell differentiation 12 map04662 B cellreceptor signaling pathway

Table 9 below lists pathways important for anti-diabetes. A high numberlisted in the “diabetes significance” column means that the pathway ishighly significant, a low number means low significance.

TABLE 9 Top Diabetes-related Pathways Diabetes KEGG Map SignificanceEntry No. Pathway Name 80 map04910 Insulin signaling pathway 64 map00010Glycolysis/ Gluconeogenesis 64 map04141 Protein processing inendoplasmic reticulum 64 map04150 mTOR signaling pathway 64 map04152AMPK signaling pathway 64 map04211 Longevity regulating pathway 64map07051 Antidiabetics 60 map04911 Insulin secretion 60 map04933AGE-RAGE signaling pathway in diabetic complications 48 map04371 Apelinsignaling pathway 36 map05417 Lipid and atherosclerosis 24 map04060Cytokine-cytokine receptor interaction 24 map04064 NF-kappa B signalingpathway 24 map04668 TNF signaling pathway 19 map04931 Insulin resistance18 map04670 Leukocyte transendothelial migration 16 map04010 MAPKsignaling pathway 16 map04145 Phagosome 16 map04614 Renin-angiotensinsystem 16 map07046 Immunosuppressive agents 16 map04979 Cholesterolmetabolism 16 map04923 Regulation of lipolysis in adipocytes 16 map04940Type I diabetes mellitus 12 map04657 IL-17 signaling pathway 11 map05418Fluid shear stress and atherosclerosis 10 map04750 Inflammatory mediatorregulation of TRP channels

To exemplify, the KEGG pathway HSA05417 contains unique pathways forthree of the cell types modelled in this work (ECs, VSMCs, andmacrophages), plus the plasma compartment. In other words, pathwayHSA5417 is one of the ones that is broken into cell-type specificpieces. In particular, the relations summarizing the products fromlow-density lipoprotein (LDL) into oxidized LDL (oxLDL), glycated LDL(glyLDL), and minimally modified LDL (mmLDL) were identified in terms ofrelations with proteins in the tissue (see, Kanehisa, M.; “Post-genomeInformatics”, Oxford University Press (2000); Otsuka et al., Pathologyof coronary atherosclerosis and thrombosis. Cardiovasc Diagn Ther 6,396-408, doi:10.21037/cdt.2016.06.01 (2016)).

HSA04514 (“Cell Adhesion Molecules”) similarly contains pathwayinformation for three modelled cell types (ECs, lymphocytes, andmacrophages) with content split accordingly. HSA04514 is another pathwaythat is broken into cell-type specific pieces.

HSA04640, “Hematopoietic Cell Lineage” was split to remove contentirrelevant for the cell types modelled in our work.

HSA04670, “Leukocyte Transendothelial Migration” splits the EC portionfrom the leukocyte portion, where two of the cell types modelled in thisstudy were leukocytes (macrophages and lymphocytes).

HSA04931, “Insulin Resistance,” is included both in VSMCs, needed forour study, and also in liver, not used in our study.

Likewise, some pathways included content relating to the plasma-tissueboundary as noted.

The resulting set of pathways was integrated into cell networks at threescopes: “core,” “mid,” “full,” utilizing a program to split .kgml filesby cell type. “Core” networks included pathways unique to eachrespective cell type. “Mid” included pathways shared by one other celltype. “Full” included pathways shared by these and other human celltypes, being in general associated with mammalian cell function. Theselected pathways at each scope for each cell type were merged into acytoscape representation using BioNSi (Biological NetworkSimulator)(Shalhoub, J. et al. Systems biology of human atherosclerosis.Vascular and endovascular surgery 48, 5-17 (2014); Fava, C. &Montagnana, M., Atherosclerosis is an inflammatory disease, which lacksa common anti-inflammatory therapy: how human genetics can help to thisissue. A narrative review. Frontiers in pharmacology 9, 55 (2018)),however, overriding the edge weights to allowed a richer set ofrelations than otherwise supported by BioNSi. The generated node listswere then compared against available plaque protein measurements fromour cohort. Proteins where no direct experimental measurement wasavailable and having no incoming edges were pruned.

BioNSi is a tool for modeling biological networks and simulating theirdiscrete-time dynamics, implemented as a Cytoscape app. BioNSi includesa visual representation of the network that enables researchers toconstruct, set the parameters, and observe network behavior undervarious conditions. In particular, specifics on the use of BioNSi namesto signify LDL products in the methods described herein include (this isnot the way BioNSi is normally intended, but is used here as a means torepresent a more granular biochemistry as needed to support thesimulations described herein):

-   -   1. glyLDL is reflected as glycosylation of LDL, as a correct        representation    -   2. oxLDL is reflected as binding/association, not because it is        the correct name, but rather that the weight is 1, indicating        that a fraction gets converted, and that oxLDL is the smallest        fraction    -   3. mmLDL is reflected as state change, not because it is the        correct name, but rather that the weight is 3, justified as        Levitan 2010 indicating that a fraction gets converted, and that        mmLDL is a higher fraction than ox    -   4. VLDL is reflected as indirect effect, not because it is the        correct name, but rather that the weight is 2, justified as VLDL        being estimated as TG/5, with 93 patients having both TG and        LDL, had an average level of 15% of LDL (as a best-effort        approximation).

FIG. 10 shows HSA05417, “Lipid and Atherosclerosis,” which containsunique pathways for three of the cell types modeled in this work (EC,VSMC, and macrophages), plus good detail in the plasma compartment. Inparticular, the relations summarizing the products from LDL intooxidized LDL (oxLDL), glycated LDL (glyLDL), and minimally areidentified in terms of relations with proteins in the tissue. Adaptedfrom the KEGG database for pathway HSA05417 (Kanehisa, M. and Goto, S.;KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 28,27-30 (2000); Kanehisa, M; Toward understanding the origin and evolutionof cellular organisms. Protein Sci. 28, 1947-1951 (2019); Kanehisa, M.,Furumichi, M., Sato, Y., Ishiguro-Watanabe, M., and Tanabe, M.; KEGG:integrating viruses and cellular organisms. Nucleic Acids Res. 49,D545-D551 (2021)).

Table 10, below shows detailed BioNSi edge mappings on import.

TABLE 10 Specific Mappings used to Achieve Result Name Weight Visualappearance Activation 10 --> (solid, arrow) inhibition −10 --| (solid,T) indirect effect 2 ..> (dash, arrow) state change 3 ... (sinewave)binding/association 1 --- (parallel lines) dissociation −1 −+− (zigzag)missing interaction 0 −/− (dots) phosphorylation 4 +p (forward slash)dephosphorylation −4 −p (backward slash) glycosylation 5 +g (contiguousarrow) ubiquitination −7 +u (solid, cross delta) methylation 6 +m(separate arrow)BioNSi import also adds self-inhibition loops (−9) but they can bedeleted when used without transcriptomic data, or can represent thetranscription/translation process when both proteomics andtranscriptomics data are used.

In addition, networks for the integrated intima were created bycompartmentalizing proteins into the intracellular of each cell type,the cell membranes, the extracellular space, with a separate compartmentfor the blood (See, FIG. 11 ). Specifically, in FIG. 11 , first leveltargets of an unstable patient at baseline (patient P491), isrepresented in a layout that highlights compartmentalization with plasma(pink hue) with serum LDL indicated to reflect relations with proteinsin the plasma membranes of ECs (green), macrophages (orange), VSMCs,(aquamarine), and in the extracellular region. A large majority ofproteins were well compartmentalized with approximately 15% localized tothe extracellular region. The intima network included 4411 proteins,after compartmentalization it was observed that as many as 1446 werelocalized to multiple cell compartments (FIGS. 11 and 12 ).

Specifically, FIG. 12 shows the integrated intima network at “full”scope for an unstable patient (patient P491) in an untreated or baselinecondition.

Example 2: Per-Patient Calibrated Networks

Given the network definitions thus created, the proteomic data was usedto update networks using calibration data from each patient.Approximately 50% of the proteins in the networks were actually measuredwithin the proteomic dataset. Since the pathways cover all selectedprotein-protein interactions in the pathway, estimation of proteinlevels for those lacking measurement in the dataset requiredinterpolation. A total of 540 personalized networks were calibrated: 2for each of the 18 patients at each cell type, integrated intima, and ateach of the three scopes, respectively, comprising the entire databaseof protein level vectors referred to as “exemplars.” The database ofexemplars demonstrated large variation in proteomic signatures afterindividual test patient calibration, corresponding to an estimated rangeof 39-96% plaque instability in the baseline condition.

Pseudo-code for the algorithm is outlined as follows:

-   -   Set true plaque phenotype from histology (minimal, stable,        unstable)    -   Load protein levels    -   Interpolate missing protein levels by iterating until achieving        a high similarity (cosine similarity metric as a measure of        convergence):        -   For each node that is not fixed:            -   For each edge, record suggestion (negating weights from                outgoing edges):                -   If the weight is negative (e.g., inhibition) if the                    source is less than the mean, unweighted suggestion                    is formed to pull down the target by a modest                    amount, or if the source is above the mean, to pull                    the target correspondingly down                -   Else (e.g., activation), if the source is less than                    the mean, unweighted suggestion is formed to raise                    the target modestly, or if greater than the mean, to                    raise it correspondingly more            -   Create a weighted mean (handling missing values)        -   Record the result and iterate for overall convergence            (handling lack of convergence)    -   Save protein levels

An example of a visualization of individual patient calibrationmolecules is shown in FIGS. 13A and 13B. FIG. 13A is a map (originallyin color) that represents those molecules that had direct measurementsfor the EC core network. Specifically, some molecules show highexpression (or red), some show low expression (or blue), and for somemolecules direct measurements were not available (green). FIG. 13Brepresents interpolated values that demonstrate propagation of levelsfrom non-interpolated proteins according to type and weight of relationdrawn from the pathway specification.

Clustering analysis performed on the calibrated networks identifiedproteins with high variance for each cell type and scope. By way ofexample, the proteins with the highest variance between unstable plaque,stable plaque, and minimal disease at the core scope for ECs wereinterstitial collagenase (MMP1), lipopolysaccharide-binding protein(LBP), advanced glycosylation end product-specific receptor (RAGE), andintegrin alpha-IIb (ITGA2B). At the mid scope, proteins such as TLR4 andHMOX1 also demonstrated large differences. For the mid scope networks,VSMCs showed strong separation in proteins such as tumour protein (p53),mothers against decapentaplegic homolog 2 (SMAD2), and coagulationfactor VIII (F8), macrophages in proteins such as lipocalin 2 (LCN2),S100 calcium binding protein (S100A8/9), and cyclin dependent kinaseinhibitor 1A (CDKN1A). In lymphocytes, matrix metalloproteinases(MMP1/9), insulin like growth factor binding protein acid labile subunit(IGFALS), and solute carrier family 2 (SLC2A1) were separated, whereasthe integrated intima showed strong separation in proteins such as SMAD2and S100A9 across cell types and interleukin 23 Receptor (IL23R) inlymphocytes.

Specifically, FIGS. 14-18 are heatmaps identifying the top 25 proteinsin terms of variance among the signatures in the experimental cohort forvarious cells. In each of the heat maps, expression levels of variousproteins are shown (red for high expression; blue for low expression).

FIG. 14 is a heatmap identifying the top 25 proteins in terms ofvariance among the signatures in our experimental cohort, in this casefor the endothelial cell, mid scope network. Strong separation inproteins such as MMP1, TLR4, HMOX1, and others is evident.

FIG. 15 is a heatmap identifying the top 25 proteins in terms ofvariance among the signatures in our experimental cohort, in this casefor the VSMC, mid scope network. Strong separation in proteins suchasTP53, SMAD2, F8, and others is evident. Note, classical markers forthe cell types are not the focus, rather, those proteins with largevariation in levels across the levels of instability.

FIG. 16 is a heatmap identifying the top 25 proteins in terms ofvariance among the signatures in our experimental cohort, in this casefor the macrophage, mid scope network. Strong separation in proteinssuch asLCN2, S100A8/9, CDKN1A, and others is evident. Note, classicalmarkers for the cell types are not the focus, rather, those proteinswith large variation in levels across the levels of instability.

FIG. 17 is a heatmap identifying the top 25 proteins in terms ofvariance among the signatures in our experimental cohort, in this casefor the lymphocyte, mid scope network. Strong separation in proteinssuch asMMP1/9, IGFALS, SLC2A1, and others is evident. Note, classicalmarkers for the cell types are not the focus, rather, those proteinswith large variation in levels across the levels of instability.

FIG. 18 is a heatmap identifying the top 25 proteins in terms ofvariance among the signatures in our experimental cohort, in this casefor the intima, mid-scope network. Strong separation in proteins suchasSMAD2 and S100A9 (across cell types), IL23R (in the lymphocytes), andseveral in the extracellular region is evident. Stable clusters betweenunstable and minimal. Note, classical markers for the cell types are notthe focus, rather, the focus is on those proteins with large variationin levels across the levels of instability.

Example 3: Treatment Dependent Network Perturbations

Based on the identified proteins from the clustering results, plaqueinstability in this cohort was found to be mainly driven by networkscoupled to endothelial dysfunction, modulated immune system responses,and inflammation at a range of degrees. Consequently, we simulatedtreatments with intensive lipid lowering (Sawada et al., From unbiasedtranscriptomics to understanding the molecular basis of atherosclerosis.Current Opinion in Lipidology 32, 328-329,doi:10.1097/mo1.0000000000000773(2021)) an IL 1β antagonist as anexample anti-inflammatory drugs (Alimohammadi et al., Development of aPatient-Specific Multi-Scale Model to Understand Atherosclerosis andCalcification Locations: Comparison with In vivo Data in an AorticDissection. Front Physiol 7, 238, doi:10.3389/fphys.2016.00238 (2016)),and an anti-diabetic agent, with hypothesized effects in treatment ofatherosclerosis (Corti, A. et al. Multiscale Computational Modeling ofVascular Adaptation: A Systems Biology Approach Using Agent-BasedModels. Front Bioeng Biotechnol 9, 744560, doi:10.3389/fbioe.2021.744560(2021); Casarin et al., A Computational Model-Based Framework to PlanClinical Experiments—an Application to Vascular Adaptation Biology.Comput Sci ICCS 10860, 352-362, doi:10.1007/978-3-319-93698-7_27(2018)).

The intensive lipid lowering treatment was modelled by decreasing thepatient's LDL level by 25% constrained by a minimal value to representclinically reported effects of such therapies (Morgan et al.,Mathematically modelling the dynamics of cholesterol metabolism andageing. Biosystems 145, 19-32, doi:10.1016/j.biosystems. 2016.05.001(2016)). For plasma lipids, we modelled LDL products (Otsuka et al.,Pathology of coronary atherosclerosis and thrombosis. Cardiovasc DiagnTher 6, 396-408, doi:10.21037/cdt.2016.06.01 (2016)) includingglycosylated (glyLDL), oxidised (oxLDL), and minimally-modified (mmLDL),and VLDL. Specifics on LDL products are outlined above.

FIGS. 19A-19B is an illustration of the intima model at the “core” scopebefore and after simulation of treatment with intensive lipid lowering.The “untreated or baseline” panel, shown in FIG. 19A indicates proteinlevels after calibration for the unstable patient in FIGS. 3A and 3D.LDL is at the center of the layout, and both direct as well as indirecteffects of lowering LDL levels by simulated therapy can be identified.Simulations with intensive lipid lowering demonstrated changes inprotein levels stemming from the decrease in LDL level and its endproducts (e.g., oxLDL), both with respect to directly affected proteinsas well effects propagated through networks. Intensive lipid loweringwas seen to decrease the levels of many proteins related to plaqueinstability, while increasing some proteins estimated to conferstability (FIG. 19B).

The anti-inflammatory treatment was modeled by holding IL1β level to aminimum level observed across proteins in the dataset. The anti-diabetictreatment was modelled by holding MTOR, NFKβ1, ICAM1, and VCAM1 (basedon documented effects of Metformin) to the minimum level observed acrossproteins in the dataset (Ally et al., Role of neuronal nitric oxidesynthase on cardiovascular functions in physiological andpathophysiological states. Nitric Oxide 102, 52-73 (2020); Parton etal., New models of atherosclerosis and multi-drug therapeuticinterventions. Bioinformatics 35, 2449-2457,doi:10.1093/bioinformatics/bty980 (2018)). “Minimum level” refers to thelowest number in the test subject data across molecules, determined as afunction of the process.

The results from this specific example showed that simulation withintensive lipid lowering therapy was generally the most effective atdecreasing plaque instability, with marginal improvement in simulatedcombination therapy. Anti-inflammatory and anti-diabetic therapiesprovided mixed results from patient to patient, manifesting as overallinferior performance compared with intensive lipid lowering. Thecombination therapy that included intensive lipid lowering and ananti-diabetic drug was in general the best for patients starting outfrom highly unstable proteomic signatures. This example illustrates thatthe invention can be an effective strategy for selected patients.Moreover, the fact that some initially unstable patients did not showappreciable response to the simulated pharmacotherapies suggested anability of the modelling approach to identify individuals best treatedsurgically rather than medically. Patients with initially stablesignatures showed less improvement by the simulated therapies,indicating sufficient preventive efficacy on standard medical treatmentalone. In addition, some patients starting with unstable signatures didnot benefit from simulated pharmacotherapy and should likely receivepreventive surgery, suggesting a potential of the modelling approach toidentify high-risk individuals and improve decision making betweenprocedural intervention and pharmacotherapy. The individualized patienttreatment recommendations differed widely across patients, highlightingthe importance of individual predictions and more refined patientstratifications, as enabled by the defined systems biology model of ourstudy. Given the dominating inflammatory proteomic signature of unstableplaques, the subtle effects observed by simulation withanti-inflammatory therapy are worth considering. This finding may be dueto the fact that only a single dose of treatment was simulated whereaseffective inhibition of inflammatory pathways would possibly require notonly sustained presence of the antagonist but also reduction in thedriving cause. In addition, the chosen treatment targeted IL1β, as thisstrategy has been shown to be effective at the group level and even moreeffectively in subgroups with enhanced systemic inflammation, which werenot included in our cohort and may thus not be well represented in theresulting model. In different cohorts or settings, response toanti-inflammatory treatment may exceed intensive lipid lowering onpatients with CVD as a comorbidity rather than as a primary indication.Nevertheless, for clinical applicability, the model should ideallycapture such phenotypes. Inclusion of patients from these subgroupswould improve efficacy, and if necessary, the model could be revisedusing indicators of enhanced systemic inflammation such as CRP. In anycase, the demonstrated superior effect of combination therapy overintensive lipid lowering alone, suggests an ability of the modellingapproach of the study to adequately simulate effects of drugs targetingdifferent pathways in disease pathophysiology.

Prediction of Subject-Specific Drug Response

Drug response was then simulated in silico. In our study, the firstcategory of simulated treatment was intensive lipid lowering,anti-inflammatory drugs (i.e., canakinumab), anti-diabetics (i.e.,metformin), and a combination of intensive lipid lowering andanti-diabetic.

Two control simulations for each subject were also computed as a checkon the mathematical formalism to prevent inadvertent design or codingdefects. The first control simulation represented no change intreatment, where the expected result was to be the same as the baselinecase but derived as if it was a treatment and running through the samesimulation; if the output was found to differ from the baseline case, alogic or mathematical error would be detected. The second controlsimulation was named “multiple insult,” which simulated a condition of a“perfect storm” of atherosclerosis risk factors causing know diseasedrivers. In this control, the expected result was to see degradedstability, roughly in proportion to the original stability, that is, thefarther the subject started form these adverse conditions the worsetheir relative impact should be. If this did not result, a logic and/ormathematical error would be detected.

Multi-Level Analysis of Simulated Treatment Effect

The simulated treated and baseline conditions were evaluated usingmulti-level analysis. Mean absolute cohort-level instabilitydemonstrated coherent estimation across cell types and scopes. Thevariation across individuals is shown in FIG. 20 , with mean effectssetting intercepts and individual variations defined by patient-specificeffects.

Further, the distribution of absolute baseline instability demonstrateda wide range across the experimental cohort (FIGS. 21A-21G).Specifically, in FIGS. 21A-21G each line indicates the particulartherapy, with the response being shown as points for each network scope.Each panel represents either the baseline condition (FIG. 21A) or thesimulated result (FIGS. 21B-21G) after the network is perturbed toreflect the effects. The use of multiple scopes is illustrated, as eachrepresents a differing sensitivity or specificity to the simulatedeffect; too sensitive can produce false positive results mitigated bythe higher scope networks, but the higher scope networks can miss theeffect, given their more inclusive set of pathways. High numbersindicate “more” unstable, i.e., a lower instability is desirable from asubject's or patient's point of view. The plots are shown as an example,other network scopes or candidate treatments are to be understoodwithout loss of generality.

Additionally, results demonstrated the mean relative treatment effects(positive indicate decreased instability our improved by treatment),across cohort, cell types, and network scopes (FIGS. 22A-22F).Specifically, FIGS. 22A-22F are plots showing a different way ofrepresenting the data that is also shown on the absolute charts, with abetter visualization of the change, not just the net effect, of thetreatments, respectively. The plots are shown as an example, networkscopes or candidate treatments are to be understood without loss ofgenerality.

In FIGS. 21 and 22 , the panels represent results for each simulatedtherapy as well as computational controls. Each curve plots the absoluteinstability (FIG. 21 ) or the relative improvement (FIG. 22 ) at eachcell type and scope. In general it may be seen that the core scopenetworks tend to show greater response to therapy than the full scopenetworks, which is expected based on the assignment of pathways wherethe more comprehensive the network the less sensitive to perturbation.Likewise, different cell type respond differently based on the nature ofthe therapeutic mechanism of action and its effect on the differenttypes. The multi-level statistical analysis uses the differences inresponse by cell type and scope to determine the significance orcertainty in the result and calculates the magnitude of effect based onthe values of the various responses, which is done to build a robustcalculation of response that is less sensitive to errors in individualmolecular levels or missing biological knowledge in the several pathwaysand their assignments to cell types.

The multi-level analysis across cell types and scopes also demonstratedcoherent estimation of mean absolute cohort-level response to treatmentin the mathematical controls.

Treatment effects ranged from an improvement of 20% to no improvement.Not only did improvement vary from patient to patient, but the range ofimprovement observed differed based on how instability was estimated.Whereas improvement in clinically symptomatic patients ranged from −8%to +20% and from −22% to +13% for asymptomatic patients; these rangestighten to −2% to +20% for patients with relatively unstable proteinlevels vs. −22% to +7% for patients with more stable protein levels.There are two important points raised by this; first, the ability todistinguish for a given patient rather than a group is motivated, andsecond, this is critically important as an improvement vs. standardclinical practice of using symptomatology to guide treatment.

Intensive lipid lowering had the strongest effect particularly inpatients starting out with unstable plaque signatures and morphology.The simulated treatment predictions showed distinct variation betweensubjects. For example, patients P491 and P773 were initiallycharacterized by highly unstable proteomic signatures, and where thebest effect of treatment simulation would be expected. Indeed, whereassimulation with intensive lowering exceeded the other monotherapies,both anti-inflammatory and anti-lipid diabetic therapies conferredimprovement, as well as simulation with the combination therapy thatexceeded the benefit of intensive lipid lowering (Table 11, FIG. 23 ,and FIG. 24 ).

Table 11, below, shows the absolute and relative improvement forbaseline and treated cases. Bold patient IDs were annotated usinghistology and clinical symptomatology as unstable. Key: Bas=baseline;ILL=Intensive Lipid Lowering; −IL1B=anti-IL1B (anti-inflammatory);Met=Metformin (anti-diabetic); Comb=Combination; Imp=improvement. pvalues: ****<0.0001, ***<0.001, **<0.01. Each patient is represented asa row, with quantitative assessment of absolute instability for thebaseline conditions and each simulated condition followed by thequantitative relative improvement. The relative improvement cells arebased on the significance of improvement, as judged by a net decrease ofinstability; +7% and above signifying statistically significantimprovement relative to baseline, −5% to +6% signifying the absence ofany statistically significant effect, and −7% and below signifyingstatistically significant degradation relative to baseline state. Rowsare sorted by baseline instability. Patients P834, P821, P298, P187, andP491 (all categorized as unstable from histological reference) are seento benefit the most from treatment, all starting from a position of veryunstable and ending up at stable post treatment. Patients P853, P450,and P737 represent highly unstable plaques showing a lack of treatmenteffects, indicating that these cases could be viewed as the most tobenefit from surgical intervention, given their highly unstablephenotypes and lack of improvement by pharmacotherapy. Patients P472,P265, and P682 represent patients for which neither pharmacotherapy willhelp nor can be needed, given the stability of the plaque.

TABLE 11 Simulated treatment effects for the Individual SubjectsRelative Improvement Absolute Instability ILL -IL1B Met Comb Patient BasILL -IL1B Met Comb Imp p Imp p Imp p Imp p P450 96% 85% 96% 96% 84% +11%**  −0% 0.5  +0% 0.5 +12% ** P491 95% 75% 88% 87% 74% +19% ****  +7%*0.05  +7% *0.05 +20% **** P853 94% 76% 89% 88% 75% +18% **  +5% 0.41 +6% 0.41 +19% ** P834 94% 81% 93% 92% 80% +13% ****  +1% 0.12  +1% 0.08+13% **** P737 92% 79% 91% 91% 78% +13% **  +1% 0.41  +1% 0.41 +13% **P773 89% 70% 82% 82% 69% +18% ****  +7% *0.05  +7% *0.05 +20% **** P18786% 72% 85% 84% 71% +14% ***  +1% 0.41  +2% 0.32 +15% *** P549 86% 76%88% 87% 75% +10% **  −2% 0.32  −1% 0.41 +11% ** P821 85% 73% 86% 85% 72%+12% **  −1% 0.41  +0% 0.5 +13% ** P298 81% 69% 78% 78% 68% +13% **  +3%0.24  +3% 0.24 +13% ** P762 76% 66% 80% 79% 64% +11% **  −3% 0.24  −2%0.32 +12% ** P836 69% 65% 79% 78% 64%  +5% 0.12  −9% *0.02  −8% *0.03 +6% 0.08 P504 68% 62% 78% 77% 61%  +6% 0.08 −10% **  −9% *0.02  +7%*0.05 P946 62% 61% 75% 74% 60%  +1% 0.41 −13% ** −12% **  +2% 0.32 P86457% 61% 75% 73% 60%  −4% 0.17 −18% **** −16% ****  −3% 0.24 P472 48% 51%67% 65% 48%  −2% 0.32 −18% **** −17% ****  +0% 0.5 P682 40% 48% 62% 61%47%  −8% *0.03 −21% **** −21% ****  −7% *0.05 P265 39% 46% 61% 61% 44% −7% *0.05 −22% **** −21% ****  −5% 0.12

Radar charts, shown in FIG. 23 , represent the degree of absoluteatherosclerotic plaque stability. Four potential treatments andmathematical two controls were simulated for each patient. The outersection (or green) indicates protein level signatures with minimaldisease, light gray (or yellow) indicates stable plaque, and dark gray(or red) indicates unstable plaque. The treatments included intensivelipid lowering, anti-inflammatory and anti-diabetics and a combinationof intensive lipid lowering and anti-diabetics. The two controls weredone to exclude mathematical errors in the model as well as to simulatethe anticipated effect of multiple insults representing maximum diseaseprogression. Each of these conditions was plotted as the absolute effecton stability.

Radar charts, shown in FIG. 24 , represent the relative improvementafter treatment simulation. For each patient, four potential treatmentsand two mathematical controls were simulated. The light gray outerregion (or green) indicates protein level signatures conferringincreased stability, and inner, dark gray region (or red) indicatesdecreased stability. The treatments included intensive lipid lowering,anti-inflammatory and anti-diabetics and a combination of intensivelipid lowering and anti-diabetics. The two controls were done to excludemathematical errors in the model as well as to simulate the anticipatedeffect of multiple insults representing maximum disease progression.Each of these conditions was plotted as the relative improvement ascompared to the untreated or baseline condition.

We next observed a relatively well-defined threshold of absoluteinstability level of approximately 76%, where subjects with greaterinstability in the baseline state showed benefit from intensive lipidlowering and improved further with combination therapy but did notbenefit when anti-inflammatory or anti-diabetic agents alone weresimulated.

One set of patients that were initially characterized as highlyunstable, did not show any response to the simulated pharmacotherapies.Importantly, this finding suggested an ability of the modelling approachof the study to identify individuals with a high risk, rather suitablefor surgical treatment than pharmacotherapy. Further, we found thatpatients with initially more stable plaque signatures either did notimprove, regardless of treatment category simulated. In thisproof-of-concept setting, combinatory treatment or intensive lipidlowering alone had a generally beneficial effect on stability, withcombination therapy providing incremental benefit for many patients.

Full results including summary of mean effects, confidence intervals,and assessment of contribution variance are provided in Table 12 andTable 13, shown below.

TABLE 12 Absolute Instability, Treated and Baseline, with ConfidenceIntervals Absolute Instability Patient Baseline Intensive Lipid LoweringCanakinumab Metformin Combination P450 0.96 [0.94, 0.99] 0.85 [0.82,0.88] 0.96 [0.93, 0.99] 0.96 [0.93,0.99] 0.84 [0.81, 0.87] P491 0.95[0.92, 0.97] 0.75 [0.72, 0.78] 0.88 [0.85, 0.91] 0.87 [0.84, 0.9] 0.74[0.71, 0.77] P853 0.94 [0.91, 0.96] 0.81 [0.78, 0.84] 0.93 [0.9, 0.96]0.92 [0.89, 0.95] 0.8 [0.77, 0.83] P834 0.94 [0.91, 0.96] 0.76 [0.73,0.79] 0.89 [0.86, 0.92] 0.88 [0.85, 0.91] 0.75 [0.72, 0.78] P737 0.92[0.9, 0.95] 0.79 [0.76, 0.82] 0.91 [0.88, 0.94] 0.91 [0.88, 0.94] 0.78[0.76, 0.81] P773 0.89 [0.86, 0.91] 0.7 [0.67, 0.73] 0.82 [0.79, 0.85]0.82 [0.78, 0.85] 0.69 [0.66, 0.72] P187 0.86 [0.84, 0.89] 0.72 [0.69,0.74] 0.85 [0.82, 0.88] 0.84 [0.81, 0.87] 0.71 [0.68, 0.74] P549 0.86[0.83, 0.89] 0.76 [0.73, 0.79] 0.88 [0.85, 0.91] 0.87 [0.84, 0.9] 0.75[0.72, 0.78] P821 0.85 [0.83, 0.88] 0.73 [0.7, 0.76] 0.86 [0.83, 0.89]0.85 [0.82, 0.88] 0.72 [0.69, 0.75] P298 0.81 [0.79, 0.84] 0.69 [0.66,0.72] 0.78 [0.75, 0.81] 0.78 [0.75, 0.81] 0.68 [0.65, 0.71] P762 0.76[0.74, 0.79] 0.66 [0.63, 0.68] 0.8 [0.77, 0.83] 0.79 [0.76, 0.82] 0.64[0.61, 0.67] P836 0.69 [0.67, 0.72] 0.65 [0.62, 0.67] 0.79 [0.76, 0.82]0.78 [0.75, 0.81] 0.64 [0.61, 0.67] P504 0.68 [0.65, 0.7] 0.62 [0.59,0.65] 0.78 [0.75, 0.81] 0.77 [0.74, 0.8] 0.61 [0.58, 0.64] P946 0.62[0.59, 0.64] 0.61 [0.58, 0.64] 0.75 [0.72, 0.78] 0.74 [0.71, 0.77] 0.6[0.57, 0.63] P864 0.57 [0.54, 0.59] 0.61 [0.58, 0.64] 0.75 [0.71, 0.78]0.73 [0.7, 0.76] 0.6 [0.57, 0.63] P472 0.48 [0.46, 0.51] 0.51 [0.48,0.54] 0.67 [0.64, 0.7] 0.65 [0.62, 0.68] 0.48 [0.46, 0.51] P682 0.4[0.37, 0.42] 0.48 [0.45, 0.51] 0.62 [0.59, 0.65] 0.61 [0.58, 0.64] 0.47[0.44, 0.5] P265 0.39 [0.36, 0.41] 0.46 [0.43, 0.49] 0.61 [0.58, 0.64]0.61 [0.57, 0.64] 0.44 [0.41, 0.47]

TABLE 13 Relative Improvement, with Confidence Intervals RelativeImprovement Pa- Intensive Lipid Lowering Canakinumab MetforminCombination tient Improvement p Improvement p Improvement p Improvementp P450 +0.11 [0.09, 0.14] **<0.01 0 [−0.03, 0.02] 0.5 0 [−0.02, 0.03]0.5 +0.12 [0.1, 0.15] **<0.01 P491 +0.19 [0.17, 0.22] ****<0.0001 +0.07[0.04, 0.09] *0.05 +0.07 [0.05, 0.1] *0.05 +0.2 [0.17, 0.23] ****<0.0001P853 +0.13 [0.1, 0.15] **<0.01 +0.01 [−0.02, 0.03] 0.41 +0.01 [−0.01,0.04] 0.41 +0.13 [0.11, 0.16] **<0.01 P834 +0.18 [0.15, 0.2] ****<0.0001+0.05 [0.02, 0.07] 0.12 +0.06 [0.03, 0.08] 0.08 +0.19 [0.16, 0.21]****<0.0001 P737 +0.13 [0.1, 0.15] **<0.01 +0.01 [−0.02, 0.03] 0.41+0.01 [−0.01, 0.04] 0.41 +0.13 [0.11, 0.16] **<0.01 P773 +0.18 [0.16,0.21] ****<0.0001 +0.07 [0.04, 0.09] *0.05 +0.07 [0.04, 0.1] *0.05 +0.2[0.17, 0.22] ****<0.0001 P187 +0.14 [0.12, 0.17] ***<0.001 +0.01 [−0.02,0.03] 0.41 +0.02 [−0.01, 0.05] 0.32 +0.15 [0.13, 0.18] ***<0.001 P549+0.1 [0.07, 0.13] **<0.01 −0.02 [−0.05, 0] 0.32 −0.01 [−0.04, 0.01] 0.41+0.11 [0.08, 0.14] **<0.01 P821 +0.12 [0.09, 0.15] **<0.01 −0.01 [−0.03,0.02] 0.41 0 [−0.03, 0.03] 0.5 +0.13 [0.11, 0.16] **<0.01 P298 +0.13[0.1, 0.15] **<0.01 +0.03 [0, 0.06] 0.24 +0.03 [0.01, 0.06] 0.24 +0.13[0.11, 0.16] **<0.01 P762 +0.11 [0.08, 0.13] **<0.01 −0.03 [−0.06,−0.01] 0.24 −0.02 [−0.05, 0] 0.32 +0.12 [0.09, 0.15] **<0.01 P836 +0.05[0.02, 0.08] 0.12 −0.09 [−0.12, −0.06] *0.02 −0.08 [−0.11, −0.05] *0.03+0.06 [0.03, 0.08] 0.08 P504 +0.06 [0.03, 0.09] 0.08 −0.1 [−0.12, −0.07]**<0.01 −0.09 [−0.11, −0.06] *0.02 +0.07 [0.04, 0.1] *0.05 P946 +0.01[−0.02, 0.03] 0.41 −0.13 [−0.16, −0.1] **<0.01 −0.12 [−0.15, −0.1]**<0.01 +0.02 [−0.01, 0.04] 0.32 P864 −0.04 [−0.06, −0.01] 0.17 −0.18[−0.2, −0.15] ****<0.0001 −0.16 [−0.19, −0.14] ****<0.0001 −0.03 [−0.06,0] 0.24 P472 −0.02 [−0.05, 0] 0.32 −0.18 [−0.21, −0.16] ****<0.0001−0.17 [−0.19, −0.14] ****<0.0001 0 [−0.03, 0.03] 0.5 P682 −0.08 [−0.1,−0.05] *0.03 −0.21 [−0.24, −0.19] ****<0.0001 −0.21 [−0.23, −0.18]****<0.0001 −0.07 [−0.09, −0.04] *0.05 P265 −0.07 [−0.09, −0.04] *0.05−0.22 [−0.25, −0.2] ****<0.0001 −0.21 [−0.24, −0.19] ****<0.0001 −0.05[−0.08, −0.03] 0.12

Personalized treatment recommendations were then composed based on thein-silico results for each patient using an automated decision algorithmwhere simulations of different pharmacotherapies were incorporated. Therecommendations combined the level of instability achieved on theselected drug choices and the controls, with a text statementautomatically generated to reflect the best therapy for that patient(FIGS. 25A-25C). Specifically, FIGS. 25A-25C show personalized patienttreatment recommendations for the three example patients, as might beprinted by a clinical decision support system incorporating thetechnique from this study. A printed or digital recommendation such asthis could be used in a patient-doctor consultation. Recommendationsgenerated by the software include one or more of the individual'sabsolute and relative radar plots, a statement on the benefit availablethrough pharmacotherapy, and one or two heatmaps representing treatedand untreated or baseline protein signatures.

Patient “John Doe” is an example of a patient with a highly unstableinitial condition that can be improved with high confidence bypharmacotherapy (FIG. 25A). The simulated treatments for patient P491demonstrated statistically significant benefit on combination therapy.The top five baseline protein levels matched four out of five unstableexemplars and one stable, thus providing strong support for an unstablestate. After recommended treatment, two minimal disease, two stable, andonly one unstable exemplar were matched, reflecting improvement bytreatment.

Patient “Bill Smith” represents a patient starting from a more stableinitial condition where pharmacotherapy would not be recommended (FIG.25B). Patient P265's baseline protein levels showed matches with fourminimal disease and one stable exemplar, indicating stability, with noimprovement after simulated treatments. The recommended therapy would beto maintain the current therapy rather than any of the simulatedtherapies.

Patient “David Jones” represents a patient that would receive onlymarginal improvement from pharmacotherapy, but based on the highlyunstable starting point, one would choose a procedural intervention asthe best course (FIG. 25C). The simulated treatments for patient P450demonstrated statistically significant benefit on combination therapy.

Heatmaps were also included for specific protein level signatures,including the protein expression for the baseline condition, and addingthe treated condition in cases where statistically significant treatmentimprovement was found. The degree to which this would be clinicallysignificant is determined from the difference in clinical presentation;treatments demonstrate a strength commensurate with the differencebetween being asymptomatic vs. symptomatic. The results for P491 andP265 illustrate the range over which the simulation capability can beapplied (FIGS. 25A-25C).

OTHER EMBODIMENTS

It is to be understood that while the invention has been described inconjunction with the detailed description thereof, the foregoingdescription is intended to illustrate and not limit the scope of theinvention, which is defined by the scope of the appended claims. Thefollowing are numbered embodiments intended to further illustrate, butnot limit, the scope of the invention.

Other aspects, advantages, and modifications are within the scope of thefollowing claims.

What is claimed is:
 1. A method of providing a recommendation of ananti-diabetic therapy for a patient with known or suspectedatherosclerotic cardiovascular disease, the method comprising: receivinga first input of non-invasively obtained imaging data related to aplaque from a set of test subjects; receiving a second input ofmolecular expression data from the set of test subjects; creating atraining set comprising the first input and the second input; training aneural network using the training set, the neural network configured topredict molecule levels based on the non-invasively obtained imagingdata from the set of test subjects; receiving non-invasively obtainedimaging data related to a plaque from the patient; generating virtual′omics data that include predicted molecule levels of the patient, byapplying the neural network to the non-invasively obtained imaging datafrom the patient; providing the virtual ′omics data to a systems biologymodel of atherosclerotic cardiovascular disease to generate apatient-specific systems biology model, wherein (i) the systems biologymodel represents a plurality of pathways associated with atheroscleroticcardiovascular disease, (ii) each pathway in the plurality of pathwayscorresponds to one or more of MTOR, NFκβ1, ICAM1, or VCAM1, (iii) thesystems biology model includes a disease-associated molecule level foreach molecule in the systems biology model, and (iv) thepatient-specific systems biology model includes predicted moleculelevels that are updated from the disease-associated molecule level;updating the patient-specific systems biology model with informationrelating to an effect on glucose levels by an anti-diabetic agent basedon a known mechanism of action of the anti-diabetic agent; andsimulating a therapeutic response by the patient to the anti-diabeticagent in the updated patient-specific systems biology model to obtain asimulated therapeutic effect, wherein the simulated therapeutic effectis based on change in the predicted molecule levels in the updatedpatient-specific systems biology model with and without theanti-diabetic agent.
 2. The method of claim 1, wherein the molecule is agene, a protein, or a metabolite.
 3. The method of claim 1, whereinsimulating the therapeutic response comprises setting decreased levelsof molecules related to plaque instability and setting increased levelsof molecules related to plaque stability in the patient-specific systemsbiology model.
 4. The method of claim 1, wherein the predicted moleculelevels comprise disease gene transcript levels, disease protein levels,or a combination of both derived from the non-invasively obtained data.5. The method of claim 1, wherein the imaging data is radiologicalimaging data obtained by computed tomography (CT), dual energy computedtomography (DECT), spectral computed tomography (spectral CT), computedtomography angiography (CTA), cardiac computed tomography angiography(CCTA), magnetic resonance imaging (MRI), multi-contrast magneticresonance imaging (multi-contrast MRI), ultrasound (US), positronemission tomography (PET), intra-vascular ultrasound (IVUS), opticalcoherence tomography (OCT), near-infrared radiation spectroscopy (NIRS),or single-photon emission tomography (SPECT) diagnostic images, or anycombination thereof.
 6. The method of claim 1, further comprisingprocessing the non-invasively obtained imaging data to obtainquantitative plaque morphology data including structural anatomy data,tissue composition data, or both.
 7. The method of claim 6, wherein thestructural anatomy data comprises data relating to a level of any one ormore of remodeling, wall thickening, ulceration, stenosis, dilation, orplaque burden.
 8. The method of claim 6, wherein the tissue compositiondata comprises data relating to a level of any one or more ofcalcification, lipid-rich necrotic core (LRNC), intraplaque hemorrhage(IPH), matrix, fibrous cap, or perivascular adipose tissue (PVAT). 9.The method of claim 1, wherein the pathways are compartmentalized intocell-specific networks.
 10. The method of claim 9, wherein thecell-specific networks include at least an endothelial cell network, amacrophage network, and a vascular smooth muscle cell network.
 11. Themethod of claim 1, wherein the anti-diabetic agent is metformin.
 12. Themethod of claim 1, further comprising recommending a combination of theanti-diabetic agent and one or both of a lipid-lowering drug and ananti-inflammatory drug.
 13. The method of claim 1, wherein simulatingthe therapeutic response for the anti-diabetic agent in thepatient-specific systems biology model comprises: determining a set ofmolecules known to be affected by the anti-diabetic agent; defining atherapeutic effect molecule level for each molecule in the set ofmolecules based on one or more known mechanisms of action of theanti-diabetic agent on the set of molecules; and estimating atherapeutic effect molecule level for molecules represented in thepatient-specific systems biology model other than in the set ofmolecules, based on a simulated effect of the defined therapeutic effectmolecule levels of the set of molecules on one or more of the othermolecules represented in the network.
 14. The method of claim 1, whereinthe systems biology model includes one or more pathways represented inTable 5 or Table 6 that are affected by glucose levels.
 15. A method ofidentifying one or more contraindications associated with ananti-diabetic therapy for a patient diagnosed with atheroscleroticcardiovascular disease, the method comprising: receiving a first inputof non-invasively obtained imaging data related to a plaque from a setof test subjects; receiving a second input of molecular expression datafrom the set of test subjects; creating a training set comprising thefirst input and the second input; training a neural network using thetraining set, the neural network configured to predict molecule levelsbased on the non-invasively obtained imaging data from the set of testsubjects; receiving non-invasively obtained imaging data related to aplaque from the patient; generating virtual ′omics data that includepredicted molecule levels of the patient, by applying the neural networkto the non-invasively obtained imaging data from the patient; providingthe virtual ′omics data to a systems biology model of atheroscleroticcardiovascular disease to generate a patient-specific systems biologymodel, wherein (i) the systems biology model represents a plurality ofpathways associated with atherosclerotic cardiovascular disease, (ii)each pathway in the plurality of pathways corresponds to one or more ofMTOR, NFκβ1, ICAM1, or VCAM1, (iii) the systems biology model includes adisease-associated molecule level for each molecule in the systemsbiology model, and (iv) the patient-specific systems biology modelincludes predicted molecule levels that are updated from thedisease-associated molecule level; updating the patient-specific systemsbiology model with information relating to an effect on glucose levelsby an anti-diabetic agent based on a known mechanism of action of theanti-diabetic agent; simulating a therapeutic response by the patient tothe anti-diabetic agent in the updated patient-specific systems biologymodel to obtain a simulated therapeutic effect, wherein the simulatedtherapeutic effect is based on change in the predicted molecule levelsin the updated patient-specific systems biology model with and withoutthe anti-diabetic agent; and identifying one or more contraindicationsassociated with the anti-diabetic agent based on change in the predictedmolecule levels in the patient-specific systems biology model with andwithout the anti-diabetic agent.
 16. The method of claim 15, wherein themolecule is a gene, a protein, or a metabolite.
 17. The method of claim15, wherein the anti-diabetic agent is metformin.
 18. The method ofclaim 15, wherein the systems biology model includes one or morepathways represented in Table 5 or Table 6 that are affected by glucoselevels.
 19. A method of screening a candidate anti-diabetic agent foratherosclerotic cardiovascular disease, the method comprising: receivinga first input of non-invasively obtained imaging data related to aplaque from a first set of test subjects; receiving a second input ofmolecular expression data from the first set of test subjects; creatinga training set comprising the first input and the second input; traininga neural network using the training set, the neural network configuredto predict molecule levels based on the non-invasively obtained imagingdata from the first set of test subjects; receiving non-invasivelyobtained imaging data related to a plaque from each of a second set oftest subjects who have been diagnosed with atheroscleroticcardiovascular disease; for each test subject of the second set of testsubjects: generating virtual ′omics data that include predicted moleculelevels of a respective test subject, by applying the neural network tothe non-invasively obtained imaging data from the respective testsubject; providing the virtual ′omics data to a systems biology model ofatherosclerotic cardiovascular disease to generate a testsubject-specific systems biology model for the respective test subject,wherein (i) the systems biology model represents a plurality of pathwaysassociated with atherosclerotic cardiovascular disease, (ii) theplurality of pathways include one or more pathways corresponding topotential targets of the candidate anti-diabetic agent, (iii) thesystems biology model includes a disease-associated molecule level foreach molecule in the systems biology model, and (iv) the testsubject-specific systems biology model includes predicted moleculelevels that are updated from the disease-associated molecule level;updating the test subject-specific systems biology model withinformation relating to an effect on glucose levels by a candidateanti-diabetic agent based on a known mechanism of action of thecandidate anti-diabetic agent; and simulating a therapeutic response bythe respective test subject to the anti-diabetic agent in the updatedtest subject-specific systems biology model to obtain a simulatedtherapeutic effect, wherein the simulated therapeutic effect is based onchange in the predicted molecule levels in the updated testsubject-specific systems biology model with and without the candidateanti-diabetic agent.
 20. The method of claim 19, wherein the molecule isa gene, a protein, or a metabolite.
 21. The method of claim 19, whereinthe anti-diabetic agent is metformin.
 22. The method of claim 19,wherein the systems biology model includes one or more pathwaysrepresented in Table 5 or Table 6 that are affected by glucose levels.23. A method of screening a potential subject for enrollment in aclinical trial testing safety or efficacy, or both, of a candidateanti-diabetic agent for atherosclerotic cardiovascular disease, themethod comprising: receiving a first input of non-invasively obtainedimaging data related to a plaque from a set of test subjects; receivinga second input of molecular expression data from the set of testsubjects; creating a training set comprising the first input and thesecond input; training a neural network using the training set, theneural network configured to predict molecule levels based on thenon-invasively obtained imaging data from the set of test subjects;receiving non-invasively obtained imaging data related to a plaque fromthe potential subject; generating virtual ′omics data that includepredicted molecule levels of the potential subject, by applying theneural network to the non-invasively obtained imaging data from thepotential subject; providing the virtual ′omics data to a systemsbiology model of atherosclerotic cardiovascular disease to generate asubject-specific systems biology model, wherein (i) the systems biologymodel represents a plurality of pathways associated with atheroscleroticcardiovascular disease, (ii) each pathway in the plurality of pathwayscorresponds to one or more of MTOR, NFκβ1, ICAM1, or VCAM1, (iii) thesystems biology model includes a disease-associated molecule level foreach molecule in the systems biology model, and (iv) thesubject-specific systems biology model includes predicted moleculelevels that are updated from the disease-associated molecule level;updating the subject-specific systems biology model with predictedmolecular levels derived from information relating to an effect onglucose by a candidate anti-diabetic agent based on a known mechanism ofaction of the candidate anti-diabetic agent; and simulating atherapeutic response by the potential subject to the candidateanti-diabetic agent in the updated subject-specific systems biologymodel to obtain a simulated therapeutic effect, wherein the simulatedtherapeutic effect is based on change in the predicted molecule levelsin the updated subject-specific systems biology model with and withoutthe candidate anti-diabetic agent.
 24. The method of claim 1, furthercomprising: providing a report, in response to determining that thesimulated therapeutic effect indicates an improvement for the patient,recommending the anti-diabetic agent for the patient.